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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'award'}) and 2 missing columns ({'doi', 'primary_area'}).
This happened while the json dataset builder was generating data using
hf://datasets/papercopilot/paperlists/acl/acl2021.json (at revision 79dc07fa205faf45ab652a7c3ee9fc9a9e61edd4)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id: string
title: string
track: string
status: string
award: bool
abstract: string
author: string
authorids: string
bibtex: string
pdf: string
site: string
pdf_size: int64
gs_citation: int64
gs_cited_by_link: string
gs_version_total: int64
aff: string
aff_domain: string
email: string
github: string
project: string
author_num: int64
aff_unique_index: string
aff_unique_norm: string
aff_unique_dep: string
aff_unique_url: string
aff_unique_abbr: string
aff_campus_unique_index: string
aff_campus_unique: string
aff_country_unique_index: string
aff_country_unique: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 3942
to
{'id': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'track': Value(dtype='string', id=None), 'status': Value(dtype='string', id=None), 'abstract': Value(dtype='string', id=None), 'primary_area': Value(dtype='string', id=None), 'author': Value(dtype='string', id=None), 'authorids': Value(dtype='string', id=None), 'aff': Value(dtype='string', id=None), 'bibtex': Value(dtype='string', id=None), 'pdf': Value(dtype='string', id=None), 'site': Value(dtype='string', id=None), 'doi': Value(dtype='string', id=None), 'pdf_size': Value(dtype='int64', id=None), 'gs_citation': Value(dtype='int64', id=None), 'gs_cited_by_link': Value(dtype='string', id=None), 'gs_version_total': Value(dtype='int64', id=None), 'aff_domain': Value(dtype='string', id=None), 'email': Value(dtype='string', id=None), 'github': Value(dtype='string', id=None), 'project': Value(dtype='string', id=None), 'author_num': Value(dtype='int64', id=None), 'aff_unique_index': Value(dtype='string', id=None), 'aff_unique_norm': Value(dtype='string', id=None), 'aff_unique_dep': Value(dtype='string', id=None), 'aff_unique_url': Value(dtype='string', id=None), 'aff_unique_abbr': Value(dtype='string', id=None), 'aff_campus_unique_index': Value(dtype='string', id=None), 'aff_campus_unique': Value(dtype='string', id=None), 'aff_country_unique_index': Value(dtype='string', id=None), 'aff_country_unique': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'award'}) and 2 missing columns ({'doi', 'primary_area'}).
This happened while the json dataset builder was generating data using
hf://datasets/papercopilot/paperlists/acl/acl2021.json (at revision 79dc07fa205faf45ab652a7c3ee9fc9a9e61edd4)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id
string | title
string | track
string | status
string | abstract
string | primary_area
string | author
string | authorids
string | aff
string | bibtex
string | pdf
string | site
string | doi
string | pdf_size
int64 | gs_citation
int64 | gs_cited_by_link
string | gs_version_total
int64 | aff_domain
string | email
string | github
string | project
string | author_num
int64 | aff_unique_index
string | aff_unique_norm
string | aff_unique_dep
string | aff_unique_url
string | aff_unique_abbr
string | aff_campus_unique_index
string | aff_campus_unique
string | aff_country_unique_index
string | aff_country_unique
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
06384
|
(Comet-) Atomic 2020: On Symbolic and Neural Commonsense Knowledge Graphs
|
main
|
Technical
|
Recent years have brought about a renewed interest in commonsense representation and reasoning in the field of natural language understanding. The development of new commonsense knowledge graphs (CSKG) has been central to these advances as their diverse facts can be used and referenced by machine learning models for tackling new and challenging tasks. At the same time, there remain questions about the quality and coverage of these resources due to the massive scale required to comprehensively encompass general commonsense knowledge. In this work, we posit that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents. Therefore, we propose a new evaluation framework for testing the utility of KGs based on how effectively implicit knowledge representations can be learned from them. With this new goal, we propose Atomic 2020, a new CSKG of general-purpose commonsense knowledge containing knowledge that is not readily available in pretrained language models. We evaluate its properties in comparison with other leading CSKGs, performing the first large-scale pairwise study of commonsense knowledge resources. Next, we show that Atomic 2020 is better suited for training knowledge models that can generate accurate, representative knowledge for new, unseen entities and events. Finally, through human evaluation, we show that the few-shot performance of GPT-3 (175B parameters), while impressive, remains ~12 absolute points lower than a BART-based knowledge model trained on Atomic 2020 despite using over 430x fewer parameters.
|
Knowledge Representation and Reasoning
|
Jena D. Hwang; Chandra Bhagavatula; Ronan Le Bras; Jeff Da; Keisuke Sakaguchi; Antoine Bosselut; Yejin Choi
|
Allen Institute for AI, WA, USA; Allen Institute for AI, WA, USA; Allen Institute for AI, WA, USA; Allen Institute for AI, WA, USA; Allen Institute for AI, WA, USA; Allen Institute for AI, WA, USA + Stanford University, CA, USA; Allen Institute for AI, WA, USA + Paul G. Allen School of Computer Science & Engineering, WA, USA + Stanford University, CA, USA
|
https://cdn.aaai.org/ojs/16792/16792-13-20286-1-2-20210518.pdf
|
https://aaai.org/papers/06384-comet-atomic-2020-on-symbolic-and-neural-commonsense-knowledge-graphs/
|
10.1609/aaai.v35i7.16792
| 216,830 | 460 |
https://scholar.google.com/scholar?cites=6819661884246092324&as_sdt=5,31&sciodt=0,31&hl=en
| 7 |
allenai.org;allenai.org;allenai.org;allenai.org;allenai.org;allenai.org;allenai.org
|
allenai.org;allenai.org;allenai.org;allenai.org;allenai.org;allenai.org;allenai.org
| 7 |
0;0;0;0;0;0+1;0+2+1
|
Allen Institute for AI;Stanford University;University of Washington
|
AI Research;;Paul G. Allen School of Computer Science & Engineering
|
https://allenai.org;https://www.stanford.edu;https://www.cs.washington.edu
|
AI2;Stanford;UW CSE
|
0;0;0;0;0;0+1;0+0+1
|
Seattle;California
|
0;0;0;0;0;0+0;0+0+0
|
United States
|
||||
09949
|
*-CFQ: Analyzing the Scalability of Machine Learning on a Compositional Task
|
main
|
Technical
|
We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying scope based on the CFQ semantic parsing benchmark, designed for principled investigation of the scalability of machine learning systems in a realistic compositional task setting. Using this suite, we conduct a series of experiments investigating the ability of Transformers to benefit from increased training data size under conditions of fixed computational cost. We show that compositional generalization remains a challenge at all training sizes, and we show that increasing the scope of natural language leads to consistently higher error rates, which are only partially offset by increased training data. We further show that while additional training data from a related domain improves the accuracy in data-starved situations, this improvement is limited and diminishes as the distance from the related domain to the target domain increases.
|
Machine Learning IV
|
Dmitry Tsarkov; Tibor Tihon; Nathan Scales; Nikola Momchev; Danila Sinopalnikov; Nathanael Schärli
|
Google Research, Brain Team; Google Research, Brain Team; Google Research, Brain Team; Google Research, Brain Team; Google Research, Brain Team; Google Research, Brain Team
|
https://cdn.aaai.org/ojs/17195/17195-13-20689-1-2-20210518.pdf
|
https://aaai.org/papers/09949-cfq-analyzing-the-scalability-of-machine-learning-on-a-compositional-task/
|
10.1609/aaai.v35i11.17195
| 355,378 | 17 |
https://scholar.google.com/scholar?cites=12187382920014923987&as_sdt=5,31&sciodt=0,31&hl=en
| 6 |
google.com;google.com;google.com;google.com;google.com;google.com
|
google.com;google.com;google.com;google.com;google.com;google.com
|
http://arxiv.org/abs/2012.08266
| 6 |
0;0;0;0;0;0
|
Google
|
Google Research
|
https://research.google
|
Google
|
0;0;0;0;0;0
|
Mountain View
|
0;0;0;0;0;0
|
United States
|
|||
09064
|
5* Knowledge Graph Embeddings with Projective Transformations
|
main
|
Technical
|
Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of subgraphs is also transformed. Most of the embedding models designed in Euclidean geometry usually support a single transformation type -- often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs. However, multi-relational knowledge graphs often include multiple subgraph structures in a neighborhood (e.g.~combinations of path and loop structures), which current embedding models do not capture well. To tackle this problem, we propose a novel KGE model 5*E in projective geometry, which supports multiple simultaneous transformations -- specifically inversion, reflection, translation, rotation, and homothety. The model has several favorable theoretical properties and subsumes the existing approaches. It outperforms them on most widely used link prediction benchmarks
|
Machine Learning III
|
Mojtaba Nayyeri; Sahar Vahdati; Can Aykul; Jens Lehmann
|
Smart Data Analytics Group, University of Bonn, Germany+Nature-Inspired Machine Intelligence-InfAI, Dresden, Germany; Nature-Inspired Machine Intelligence-InfAI, Dresden, Germany; Smart Data Analytics Group, University of Bonn, Germany; Smart Data Analytics Group, University of Bonn, Germany+Fraunhofer IAIS, Dresden, Germany
|
https://cdn.aaai.org/ojs/17095/17095-13-20589-1-2-20210518.pdf
|
https://aaai.org/papers/09064-5-knowledge-graph-embeddings-with-projective-transformations/
|
10.1609/aaai.v35i10.17095
| 12,732,732 | 41 |
https://scholar.google.com/scholar?cites=852693485803819962&as_sdt=2005&sciodt=0,5&hl=en
| 8 |
cs.uni-bonn.de;infai.org;cs.uni-bonn.de;cs.uni-bonn.de+jens.lehmann
|
cs.uni-bonn.de;infai.org;cs.uni-bonn.de;cs.uni-bonn.de+jens.lehmann
| 4 |
0+1;1;0;0+2
|
University of Bonn;Nature-Inspired Machine Intelligence-InfAI;Fraunhofer Institute for Intelligent Analysis and Information Systems
|
Smart Data Analytics Group;;
|
https://www.uni-bonn.de;;https://www.iais.fraunhofer.de/
|
;;Fraunhofer IAIS
|
1;1;1
|
;Dresden
|
0+0;0;0;0+0
|
Germany
|
||||
08384
|
A Bayesian Approach for Subset Selection in Contextual Bandits
|
main
|
Technical
|
Subset selection in Contextual Bandits (CB) is an important task in various applications such as advertisement recommendation. In CB, arms are attached with contexts and thus correlated in the context space. Proper exploration for subset selection in CB should carefully consider the contexts. Previous works mainly concentrate on the best one arm identification in linear bandit problems, where the expected rewards are linearly dependent on the contexts. However, these methods highly rely on linearity, and cannot be easily extended to more general cases. We propose a novel Bayesian approach for subset selection in general CB where the reward functions can be nonlinear. Our method provides a principled way to employ contextual information and efficiently explore the arms. For cases with relatively smooth posteriors, we give theoretical results that are comparable to previous works. For general cases, we provide a calculable approximate variant. Empirical results show the effectiveness of our method on both linear bandits and general CB.
|
Machine Learning II
|
Jialian Li; Chao Du; Jun Zhu
|
Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Lab, Bosch-Tsinghua Joint ML Center, Tsinghua University; Alibaba Group; Dept. of Comp. Sci. & Tech., Institute for AI, BNRist Lab, Bosch-Tsinghua Joint ML Center, Tsinghua University
|
https://cdn.aaai.org/ojs/17019/17019-13-20513-1-2-20210518.pdf
|
https://aaai.org/papers/08384-a-bayesian-approach-for-subset-selection-in-contextual-bandits/
|
10.1609/aaai.v35i9.17019
| 351,084 | 1 |
https://scholar.google.com/scholar?cites=10827821828633626655&as_sdt=5,33&sciodt=0,33&hl=en
| 3 |
mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn
|
mails.tsinghua.edu.cn;gmail.com;tsinghua.edu.cn
| 3 |
0;1;0
|
Tsinghua University;Alibaba Group
|
Dept. of Comp. Sci. & Tech.;
|
https://www.tsinghua.edu.cn;https://www.alibaba.com
|
THU;Alibaba
|
0;0;0
|
China
|
||||||
13889
|
A Bidirectional Multi-paragraph Reading Model for Zero-shot Entity Linking
|
main
|
Technical
|
Recently, a zero-shot entity linking task is introduced to challenge the generalization ability of entity linking models. In this task, mentions must be linked to unseen entities and only the textual information is available. In order to make full use of the documents, previous work has proposed a BERT-based model which can only take fixed length of text as input. However, the key information for entity linking may exist in nearly everywhere of the documents thus the proposed model cannot capture them all. To leverage more textual information and enhance text understanding capability, we propose a bidirectional multi-paragraph reading model for the zero-shot entity linking task. Firstly, the model treats the mention context as a query and matches it with multiple paragraphs of the entity description documents. Then, the mention-aware entity representation obtained from the first step is used as a query to match multiple paragraphs in the document containing the mention through an entity-mention attention mechanism. In particular, a new pre-training strategy is employed to strengthen the representative ability. Experimental results show that our bidirectional model can capture long-range context dependencies and outperform the baseline model by 3-4% in terms of accuracy.
|
Speech and Natural Language Processing II
|
Hongyin Tang; Xingwu Sun; Beihong Jin; Fuzheng Zhang
|
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences + University of Chinese Academy of Sciences, Beijing China; Meituan-Dianping Group, China; State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences + University of Chinese Academy of Sciences, Beijing China; Meituan-Dianping Group, China
|
https://cdn.aaai.org/ojs/17636/17636-13-21130-1-2-20210518.pdf
|
https://aaai.org/papers/13889-a-bidirectional-multi-paragraph-reading-model-for-zero-shot-entity-linking/
|
10.1609/aaai.v35i15.17636
| 1,055,109 | 25 |
https://scholar.google.com/scholar?cites=6791189673009278347&as_sdt=5,33&sciodt=0,33&hl=en
| 4 |
otcaix.iscas.ac.cn;meituan.com;iscas.ac.cn;meituan.com
|
otcaix.iscas.ac.cn;meituan.com;iscas.ac.cn;meituan.com
| 4 |
0+1;2;0+1;2
|
Chinese Academy of Sciences;University of Chinese Academy of Sciences;Meituan-Dianping
|
Institute of Software;;
|
http://www.ios.ac.cn;http://www.ucas.ac.cn;https://www.meituan.com
|
CAS;UCAS;Meituan-Dianping
|
1;1
|
;Beijing
|
0+0;0;0+0;0
|
China
|
||||
06868
|
A Blind Block Term Decomposition of High Order Tensors
|
main
|
Technical
|
Tensor decompositions have found many applications in signal processing, data mining, machine learning, etc. In particular, the block term decomposition (BTD), which is a generalization of CP decomposition and Tucker decomposition/HOSVD, has been successfully used for the compression and acceleration of neural networks. However, computing BTD is NP-hard, and optimization based methods usually suffer from slow convergence or even fail to converge, which limits the applications of BTD. This paper considers a “blind” block term decomposition (BBTD) of high order tensors, in which the block diagonal structure of the core tensor is unknown. Our contributions include: 1) We establish the necessary and sufficient conditions for the existence of BTD, characterize the condition when a BTD solves the BBTD problem, and show that the BBTD is unique under a “low rank” assumption. 2) We propose an algebraic method to compute the BBTD. This method transforms the problem of determining the block diagonal structure of the core tensor into a clustering problem of complex numbers, in polynomial time. And once the clustering problem is solved, the BBTD can be obtained via computing several matrix decompositions. Numerical results show that our method is able to compute the BBTD, even in the presence of noise to some extent, whereas optimization based methods (e.g., MINF and NLS in TENSORLAB) may fail to converge.
|
Machine Learning I
|
Yunfeng Cai; Ping Li
|
Cognitive Computing Lab, Baidu Research, No. 10 Xibeiwang East Road, Beijing 100193, China; Cognitive Computing Lab, Baidu Research, 10900 NE 8th St. Bellevue, Washington 98004, USA
|
https://cdn.aaai.org/ojs/16847/16847-13-20341-1-2-20210518.pdf
|
https://aaai.org/papers/06868-a-blind-block-term-decomposition-of-high-order-tensors/
|
10.1609/aaai.v35i8.16847
| 331,177 | 5 |
https://scholar.google.com/scholar?cites=13227824535285624139&as_sdt=80005&sciodt=0,11&hl=en
| 5 |
baidu.com;baidu.com
|
baidu.com;baidu.com
| 2 |
0;0
|
Baidu Research
|
Cognitive Computing Lab
|
https://baidu.com
|
Baidu
|
0;1
|
Beijing;Bellevue
|
0;1
|
China;United States
|
||||
00039
|
A Bottom-Up DAG Structure Extraction Model for Math Word Problems
|
main
|
Technical
|
Research on automatically solving mathematical word problems (MWP) has a long history. Most recent works adopt Seq2Seq approach to predict the result equations as a sequence of quantities and operators. Although result equations can be written as a sequence, it is essentially a structure. More precisely, it is a Direct Acyclic Graph (DAG) whose leaf nodes are the quantities, and internal and root nodes are arithmetic or comparison operators. In this paper, we propose a novel Seq2DAG approach to extract the equation set directly as a DAG structure. It is extracted in a bottom-up fashion by aggregating quantities and sub-expressions layer by layer iteratively. The advantages of our approach approach are three-fold: it is intrinsically suitable to solve multivariate problems, it always outputs valid structure, and its computation satisfies commutative law for +, x and =. Experimental results on Math23K and DRAW1K demonstrate that our model outperforms state-of-the-art deep learning methods. We also conduct detailed analysis on the results to show the strengths and limitations of our approach.
|
Application Domains
|
Yixuan Cao; Feng Hong; Hongwei Li; Ping Luo
|
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China; Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China+University of Chinese Academy of Sciences, Beijing 100049, China+Peng Cheng Laboratory, Shenzhen, China
|
https://cdn.aaai.org/ojs/16075/16075-13-19569-1-2-20210518.pdf
|
https://aaai.org/papers/00039-a-bottom-up-dag-structure-extraction-model-for-math-word-problems/
|
10.1609/aaai.v35i1.16075
| 1,758,003 | 58 |
https://scholar.google.com/scholar?cites=11690626614215530510&as_sdt=2005&sciodt=0,5&hl=en
| 3 |
ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn
|
ict.ac.cn;ict.ac.cn;ict.ac.cn;ict.ac.cn
| 4 |
0+1;0+1;0+1;0+1+2
|
Chinese Academy of Sciences;University of Chinese Academy of Sciences;Peng Cheng Laboratory
|
Institute of Computing Technology;;
|
http://www.cas.ac.cn;http://www.ucas.ac.cn;
|
CAS;UCAS;
|
0+0;0+0;0+0;0+0+1
|
Beijing;Shenzhen
|
0+0;0+0;0+0;0+0+0
|
China
|
||||
03181
|
A Case Study of the Shortcut Effects in Visual Commonsense Reasoning
|
main
|
Technical
|
Visual reasoning and question-answering have gathered attention in recent years. Many datasets and evaluation protocols have been proposed; some have been shown to contain bias that allows models to ``cheat'' without performing true, generalizable reasoning. A well-known bias is dependence on language priors (frequency of answers) resulting in the model not looking at the image. We discover a new type of bias in the Visual Commonsense Reasoning (VCR) dataset. In particular we show that most state-of-the-art models exploit co-occurring text between input (question) and output (answer options), and rely on only a few pieces of information in the candidate options, to make a decision. Unfortunately, relying on such superficial evidence causes models to be very fragile. To measure fragility, we propose two ways to modify the validation data, in which a few words in the answer choices are modified without significant changes in meaning. We find such insignificant changes cause models' performance to degrade significantly. To resolve the issue, we propose a curriculum-based masking approach, as a mechanism to perform more robust training. Our method improves the baseline by requiring it to pay attention to the answers as a whole, and is more effective than prior masking strategies.
|
Computer Vision III
|
Keren Ye; Adriana Kovashka
|
University of Pittsburgh, Pittsburgh PA 15260, USA; University of Pittsburgh, Pittsburgh PA 15260, USA
|
https://cdn.aaai.org/ojs/16428/16428-13-19922-1-2-20210518.pdf
|
https://aaai.org/papers/03181-a-case-study-of-the-shortcut-effects-in-visual-commonsense-reasoning/
|
10.1609/aaai.v35i4.16428
| 8,638,003 | 49 |
https://scholar.google.com/scholar?cites=15530774569302847723&as_sdt=5,44&sciodt=0,44&hl=en
| 6 |
cs.pitt.edu;cs.pitt.edu
|
cs.pitt.edu;cs.pitt.edu
| 2 |
0;0
|
University of Pittsburgh
|
https://www.pitt.edu
|
Pitt
|
0;0
|
Pittsburgh
|
0;0
|
United States
|
|||||
11990
|
A Complexity-theoretic Analysis of Green Pickup-and-Delivery Problems
|
main
|
Technical
|
In a Green Pickup-and-Delivery problem (GPD), vehicles traveling in a transport network achieving pickup-and-delivery tasks are in particular subject to the two textit{green} constraints: limited vehicle fuel capacity thus short vehicle traveling range, and limited availability of refueling infrastructure for the vehicles. GPD adds additional but probably insignificant computational complexity to the classic and already NP-hard Pickup-and-Delivery problem and Vehicle Routing Problem. Nevertheless, we demonstrate in this paper an inherent intractability of these green components themselves. More precisely, we show that GPD problems whose total constraints are reduced to almost the green ones only, remain to be NP-complete in the strong sense. We figure out a specifically constrained variant of GPD that, however, is weakly NP-complete -- a practical pseudo-polynomial time algorithm solving the variant problem is identified. Insight obtained from this complexity-theoretic analysis would shed light for a deeper understanding of GPDs, and on better development of heuristics for solving these problems, leading to promisingly many real-world applications.
|
Planning, Routing, and Scheduling
|
Xing Tan; Jimmy Xiangji Huang
|
Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Ontario, Canada; Information Retrieval and Knowledge Management Research Lab, York University, Toronto, Ontario, Canada
|
https://cdn.aaai.org/ojs/17424/17424-13-20918-1-2-20210518.pdf
|
https://aaai.org/papers/11990-a-complexity-theoretic-analysis-of-green-pickup-and-delivery-problems/
|
10.1609/aaai.v35i13.17424
| 188,344 | 2 |
https://scholar.google.com/scholar?cites=8367154752363576720&as_sdt=2005&sciodt=0,5&hl=en
| 3 |
yorku.ca;yorku.ca
|
yorku.ca;yorku.ca
| 2 |
0;0
|
York University
|
Information Retrieval and Knowledge Management Research Lab
|
https://www.yorku.ca
|
York U
|
0;0
|
Toronto
|
0;0
|
Canada
|
||||
06030
|
A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation
|
main
|
Technical
|
Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but the results provided by fully automatic algorithms are not always guaranteed satisfactory to users. Interactive segmentation offers a solution by accepting user annotations on selective areas of the images to refine the segmentation results. However, most existing models only focus on correcting the current image's misclassified pixels, with no knowledge carried over to other images. In this work, we formulate interactive image segmentation as a continual learning problem and propose a framework to effectively learn from user annotations, aiming to improve the segmentation on both the current image and unseen images in future tasks while avoiding deteriorated performance on previously-seen images. It employs a probabilistic mask to control the neural network's kernel activation and extract the most suitable features for segmenting images in each task. We also apply a task-aware embedding to automatically infer the optimal kernel activation for initial segmentation and subsequent refinement. Interactions with users are guided through multi-source uncertainty estimation so that users can focus on the most important areas to minimize the overall manual annotation effort. Experiments are performed on both medical and natural image datasets to illustrate the proposed framework's effectiveness on basic segmentation performance, forward knowledge transfer, and backward knowledge transfer.
|
Humans and AI
|
Ervine Zheng; Qi Yu; Rui Li; Pengcheng Shi; Anne Haake
|
Rochester Institute of Technology; Rochester Institute of Technology; Rochester Institute of Technology; Rochester Institute of Technology; Rochester Institute of Technology
|
https://cdn.aaai.org/ojs/16752/16752-13-20246-1-2-20210518.pdf
|
https://aaai.org/papers/06030-a-continual-learning-framework-for-uncertainty-aware-interactive-image-segmentation/
|
10.1609/aaai.v35i7.16752
| 1,506,768 | 29 |
https://scholar.google.com/scholar?cites=17882479932607746296&as_sdt=2005&sciodt=0,5&hl=en
| 4 |
rit.edu;rit.edu;rit.edu;rit.edu;rit.edu
|
rit.edu;rit.edu;rit.edu;rit.edu;rit.edu
| 5 |
0;0;0;0;0
|
Rochester Institute of Technology
|
https://www.rit.edu
|
RIT
|
0;0;0;0;0
|
United States
|
|||||||
14085
|
A Controllable Model of Grounded Response Generation
|
main
|
Technical
|
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity to "hallucinate" facts. While this may be mitigated by access to background knowledge, there is scant guarantee of relevance and informativeness in generated responses. We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. Quantitative and qualitative results show that, using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.
|
Speech and Natural Language Processing III
|
Zeqiu Wu; Michel Galley; Chris Brockett; Yizhe Zhang; Xiang Gao; Chris Quirk; Rik Koncel-Kedziorski; Jianfeng Gao; Hannaneh Hajishirzi; Mari Ostendorf; Bill Dolan
|
University of Washington, Seattle, WA, USA; Microsoft Research, Redmond, WA, USA; Microsoft Research, Redmond, WA, USA; Microsoft Research, Redmond, WA, USA; Microsoft Research, Redmond, WA, USA; Microsoft Research, Redmond, WA, USA; University of Washington, Seattle, WA, USA; Microsoft Research, Redmond, WA, USA; University of Washington, Seattle, WA, USA+Allen Institute for AI, Seattle, WA, USA; University of Washington, Seattle, WA, USA; Microsoft Research, Redmond, WA, USA
|
https://cdn.aaai.org/ojs/17658/17658-13-21152-1-2-20210518.pdf
|
https://aaai.org/papers/14085-a-controllable-model-of-grounded-response-generation/
|
10.1609/aaai.v35i16.17658
| 1,007,395 | 91 |
https://scholar.google.com/scholar?cites=17853914586957517570&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
uw.edu;microsoft.com; ; ; ; ; ; ; ; ;
|
uw.edu;microsoft.com; ; ; ; ; ; ; ; ;
| 11 |
0;1;1;1;1;1;0;1;0+2;0;1
|
University of Washington;Microsoft Research;Allen Institute for AI
|
;;
|
https://www.washington.edu;https://www.microsoft.com/en-us/research;https://allenai.org
|
UW;MSR;AI2
|
0;1;1;1;1;1;0;1;0+0;0;1
|
Seattle;Redmond
|
0;0;0;0;0;0;0;0;0+0;0;0
|
United States
|
||||
06279
|
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
|
main
|
Technical
|
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to obviate the need for such heuristics, however, its deployment in automated theorem proving remains a challenge. In this paper we introduce TRAIL, a system that applies deep reinforcement learning to saturation-based theorem proving. TRAIL leverages (a) a novel neural representation of the state of a theorem prover and (b) a novel characterization of the inference selection process in terms of an attention-based action policy. We show through systematic analysis that these mechanisms allow TRAIL to significantly outperform previous reinforcement-learning-based theorem provers on two benchmark datasets for first-order logic automated theorem proving (proving around 15% more theorems).
|
Knowledge Representation and Reasoning
|
Maxwell Crouse; Ibrahim Abdelaziz; Bassem Makni; Spencer Whitehead; Cristina Cornelio; Pavan Kapanipathi; Kavitha Srinivas; Veronika Thost; Michael Witbrock; Achille Fokoue
|
Northwestern University; IBM Research; IBM Research; University of Illinois at Urbana-Champaign; IBM Research; IBM Research; IBM Research; MIT-IBM Watson AI Lab+IBM Research; The University of Auckland; IBM Research
|
https://cdn.aaai.org/ojs/16780/16780-13-20274-1-2-20210518.pdf
|
https://aaai.org/papers/06279-a-deep-reinforcement-learning-approach-to-first-order-logic-theorem-proving/
|
10.1609/aaai.v35i7.16780
| 541,475 | 41 |
https://scholar.google.com/scholar?cites=11076789132320124452&as_sdt=2005&sciodt=0,5&hl=en
| 9 |
u.northwestern.edu;ibm.com;ibm.com; ;zurich.ibm.com;us.ibm.com;ibm.com;ibm.com;auckland.ac.nz;us.ibm.com
|
u.northwestern.edu;ibm.com;ibm.com; ;zurich.ibm.com;us.ibm.com;ibm.com;ibm.com;auckland.ac.nz;us.ibm.com
| 10 |
0;1;1;2;1;1;1;3+1;4;1
|
Northwestern University;IBM;University of Illinois at Urbana-Champaign;Massachusetts Institute of Technology;The University of Auckland
|
;IBM Research;;MIT-IBM Watson AI Lab;
|
https://www.northwestern.edu;https://www.ibm.com/research;https://illinois.edu;https://www.mitibmwatsonailab.org;https://www.auckland.ac.nz
|
NU;IBM;UIUC;MIT-IBM AI Lab;UoA
|
1;
|
;Urbana-Champaign
|
0;0;0;0;0;0;0;0+0;1;0
|
United States;New Zealand
|
||||
09481
|
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
|
main
|
Technical
|
Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. We study the layerwise loss landscape by studying the eigenspectra of the Hessian at each layer. In particular, our results show that the layerwise Hessian geometry is largely similar to the entire Hessian. We also report an interesting phenomenon where the Hessian eigenspectrum of middle layers of the deep neural network are observed to most similar to the overall Hessian eigenspectrum. We also show that the maximum eigenvalue and the trace of the Hessian (both full network and layerwise) reduce as training of the network progresses. We leverage on these observations to propose a new regularizer based on the trace of the layerwise Hessian. Penalizing the trace of the Hessian at every layer indirectly forces Stochastic Gradient Descent to converge to flatter minima, which are shown to have better generalization performance. In particular, we show that such a layerwise regularizer can be leveraged to penalize the middlemost layers alone, which yields promising results. Our empirical studies on well-known deep nets across datasets support the claims of this work.
|
Machine Learning IV
|
Adepu Ravi Sankar; Yash Khasbage; Rahul Vigneswaran; Vineeth N Balasubramanian
|
Dept of Computer Science & Engineering, Indian Institute of Technology Hyderabad, India; Dept of Computer Science & Engineering, Indian Institute of Technology Hyderabad, India; Dept of Computer Science & Engineering, Indian Institute of Technology Hyderabad, India; Dept of Computer Science & Engineering, Indian Institute of Technology Hyderabad, India
|
https://cdn.aaai.org/ojs/17142/17142-13-20636-1-2-20210518.pdf
|
https://aaai.org/papers/09481-a-deeper-look-at-the-hessian-eigenspectrum-of-deep-neural-networks-and-its-applications-to-regularization/
|
10.1609/aaai.v35i11.17142
| 3,953,882 | 48 |
https://scholar.google.com/scholar?cites=1031869809943034120&as_sdt=2005&sciodt=0,5&hl=en
| 8 |
iith.ac.in;iith.ac.in;gmail.com;cse.iith.ac.in
|
iith.ac.in;iith.ac.in;gmail.com;cse.iith.ac.in
| 4 |
0;0;0;0
|
Indian Institute of Technology Hyderabad
|
Dept of Computer Science & Engineering
|
https://www.iith.ac.in
|
IIT Hyderabad
|
0;0;0;0
|
Hyderabad
|
0;0;0;0
|
India
|
||||
12217
|
A Fast Exact Algorithm for the Resource Constrained Shortest Path Problem
|
main
|
Technical
|
Resource constrained path finding is a well studied topic in AI, with real-world applications in different areas such as transportation and robotics. This paper introduces several heuristics in the resource constrained path finding context that significantly improve the algorithmic performance of the initialisation phase and the core search. We implement our heuristics on top of a bidirectional A* algorithm and evaluate them on a set of large instances. The experimental results show that, for the first time in the context of constrained path finding, our fast and enhanced algorithm can solve all of the benchmark instances to optimality, and compared to the state of the art algorithms, it can improve existing runtimes by up to four orders of magnitude on large-size network graphs.
|
Search and Optimization
|
Saman Ahmadi; Guido Tack; Daniel D. Harabor; Philip Kilby
|
Department of Data Science and Artificial Intelligence, Monash University, Australia+CSIRO Data61, Australia; Department of Data Science and Artificial Intelligence, Monash University, Australia; Department of Data Science and Artificial Intelligence, Monash University, Australia; CSIRO Data61, Australia
|
https://cdn.aaai.org/ojs/17450/17450-13-20944-1-2-20210518.pdf
|
https://aaai.org/papers/12217-a-fast-exact-algorithm-for-the-resource-constrained-shortest-path-problem/
|
10.1609/aaai.v35i14.17450
| 158,664 | 24 |
https://scholar.google.com/scholar?cites=15886903724494268959&as_sdt=2005&sciodt=0,5&hl=en
| 8 |
monash.edu;monash.edu;monash.edu;data61.csiro.au
|
monash.edu;monash.edu;monash.edu;data61.csiro.au
| 4 |
0+1;0;0;1
|
Monash University;CSIRO Data61
|
Department of Data Science and Artificial Intelligence;
|
https://www.monash.edu;https://www.csiro.au/en/Research/Data61
|
Monash;CSIRO Data61
|
0+0;0;0;0
|
Australia
|
||||||
05078
|
A Few Queries Go a Long Way: Information-Distortion Tradeoffs in Matching
|
main
|
Technical
|
We consider the one-sided matching problem, where n agents have preferences over n items, and these preferences are induced by underlying cardinal valuation functions. The goal is to match every agent to a single item so as to maximize the social welfare. Most of the related literature, however, assumes that the values of the agents are not a priori known, and only access to the ordinal preferences of the agents over the items is provided. Consequently, this incomplete information leads to loss of efficiency, which is measured by the notion of distortion. In this paper, we further assume that the agents can answer a small number of queries, allowing us partial access to their values. We study the interplay between elicited cardinal information (measured by the number of queries per agent) and distortion for one-sided matching, as well as a wide range of well-studied related problems. Qualitatively, our results show that with a limited number of queries, it is possible to obtain significant improvements over the classic setting, where only access to ordinal information is given.
|
Game Theory and Economic Paradigms
|
Georgios Amanatidis; Georgios Birmpas; Aris Filos-Ratsikas; Alexandros A. Voudouris
|
Department of Mathematical Sciences, University of Essex + ILLC, University of Amsterdam; Department of Computer, Control and Management Engineering, Sapienza University of Rome; Department of Computer Science, University of Liverpool; School of Computer Science and Electronic Engineering, University of Essex
|
https://cdn.aaai.org/ojs/16642/16642-13-20136-1-2-20210518.pdf
|
https://aaai.org/papers/05078-a-few-queries-go-a-long-way-information-distortion-tradeoffs-in-matching/
|
10.1609/aaai.v35i6.16642
| 169,342 | 33 |
https://scholar.google.com/scholar?cites=11819060573758966917&as_sdt=5,33&sciodt=0,33&hl=en
| 19 |
essex.ac.uk;diag.uniroma1.it;liverpool.ac.uk;essex.ac.uk
|
essex.ac.uk;diag.uniroma1.it;liverpool.ac.uk;essex.ac.uk
| 4 |
0+1;2;3;0
|
University of Essex;University of Amsterdam;Sapienza University of Rome;University of Liverpool
|
Department of Mathematical Sciences;ILLC;Department of Computer, Control and Management Engineering;Department of Computer Science
|
https://www.essex.ac.uk;https://www.uva.nl;https://www.uniroma1.it;https://www.liverpool.ac.uk
|
Essex;UvA;Sapienza;Liv Uni
|
1;2
|
;Amsterdam;Rome
|
0+1;2;0;0
|
United Kingdom;Netherlands;Italy
|
||||
08101
|
A Flexible Framework for Communication-Efficient Machine Learning
|
main
|
Technical
|
With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the bottleneck between CPUs and GPUs, but communication-efficiency is now needed in a variety of different system architectures, from high-performance clusters to energy-constrained IoT devices. In the current practice, compression levels are typically chosen before training and settings that work well for one task may be vastly suboptimal for another dataset on another architecture. In this paper, we propose a flexible framework which adapts the compression level to the true gradient at each iteration, maximizing the improvement in the objective function that is achieved per communicated bit. Our framework is easy to adapt from one technology to the next by modeling how the communication cost depends on the compression level for the specific technology. Theoretical results and practical experiments indicate that the automatic tuning strategies significantly increase communication efficiency on several state-of-the-art compression schemes.
|
Machine Learning II
|
Sarit Khirirat; Sindri Magnússon; Arda Aytekin; Mikael Johansson
|
Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden; Department of Computer and System Science, Stockholm University, Sweden; Ericsson, Sweden; Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden
|
https://cdn.aaai.org/ojs/16987/16987-13-20481-1-2-20210518.pdf
|
https://aaai.org/papers/08101-a-flexible-framework-for-communication-efficient-machine-learning/
|
10.1609/aaai.v35i9.16987
| 536,511 | 17 |
https://scholar.google.com/scholar?cites=4024620966685126056&as_sdt=2005&sciodt=0,5&hl=en
| 8 |
kth.se;dsv.su.se;aytekin.biz;kth.se
|
kth.se;dsv.su.se;aytekin.biz;kth.se
| 4 |
0;1;2;0
|
KTH Royal Institute of Technology;Stockholm University;Ericsson
|
Division of Decision and Control Systems;Department of Computer and System Science;
|
https://www.kth.se;https://www.su.se;https://www.ericsson.com
|
KTH;SU;
|
0;0;0;0
|
Sweden
|
||||||
08474
|
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
|
main
|
Technical
|
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.
|
Machine Learning III
|
Xianfeng Li; Weijie Chen; Di Xie; Shicai Yang; Peng Yuan; Shiliang Pu; Yueting Zhuang
|
South China University of Technology; Hikvision Research Institute + Zhejiang University; Hikvision Research Institute; Hikvision Research Institute; Hikvision Research Institute; Hikvision Research Institute + Zhejiang University; Zhejiang University
|
https://cdn.aaai.org/ojs/17029/17029-13-20523-1-2-20210518.pdf
|
https://aaai.org/papers/08474-a-free-lunch-for-unsupervised-domain-adaptive-object-detection-without-source-data/
|
10.1609/aaai.v35i10.17029
| 3,851,647 | 168 |
https://scholar.google.com/scholar?cites=6980672955157492261&as_sdt=5,44&sciodt=0,44&hl=en
| 5 |
163.com;hikvision.com;hikvision.com;hikvision.com;hikvision.com;hikvision.com;zju.edu.cn
|
163.com;hikvision.com;hikvision.com;hikvision.com;hikvision.com;hikvision.com;zju.edu.cn
| 7 |
0;1+2;1;1;1;1+2;2
|
South China University of Technology;Hikvision Research Institute;Zhejiang University
|
;;
|
https://www.scut.edu.cn;https://www.hikvision.com/cn/;https://www.zju.edu.cn
|
SCUT;Hikvision;ZJU
|
;
|
0;0+0;0;0;0;0+0;0
|
China
|
|||||
08992
|
A General Class of Transfer Learning Regression without Implementation Cost
|
main
|
Technical
|
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretrained neural networks. Moreover, the proposed method can benefit from its simple implementation without any additional cost; the regression model can be fully trained using off-the-shelf libraries for supervised learning in which the original output variable is simply transformed to a new output variable. We demonstrate its simplicity, generality, and applicability using various real data applications.
|
Machine Learning III
|
Shunya Minami; Song Liu; Stephen Wu; Kenji Fukumizu; Ryo Yoshida
|
The Graduate University for Advanced Studies (SOKENDAI) + The Institute of Statistical Mathematics + National Institute for Materials Science; University of Bristol; The Graduate University for Advanced Studies (SOKENDAI) + The Institute of Statistical Mathematics; The Graduate University for Advanced Studies (SOKENDAI) + The Institute of Statistical Mathematics + National Institute for Materials Science; The Graduate University for Advanced Studies (SOKENDAI) + The Institute of Statistical Mathematics + National Institute for Materials Science
|
https://cdn.aaai.org/ojs/17087/17087-13-20581-1-2-20210518.pdf
|
https://aaai.org/papers/08992-a-general-class-of-transfer-learning-regression-without-implementation-cost/
|
10.1609/aaai.v35i10.17087
| 619,157 | 9 |
https://scholar.google.com/scholar?cites=8855523077994561147&as_sdt=5,33&sciodt=0,33&hl=en
| 6 |
ism.ac.jp;bristol.ac.uk;ism.ac.jp;ism.ac.jp;ism.ac.jp
|
ism.ac.jp;bristol.ac.uk;ism.ac.jp;ism.ac.jp;ism.ac.jp
| 5 |
0+1+2;3;0+1;0+1+2;0+1+2
|
The Graduate University for Advanced Studies;The Institute of Statistical Mathematics;National Institute for Materials Science;University of Bristol
|
;;;
|
https://www.soken.kyoto-u.ac.jp;https://www.ism.ac.jp;https://www.nims.go.jp;https://www.bristol.ac.uk
|
SOKENDAI;ISM;NIMS;Bristol
|
;;;
|
0+0+0;1;0+0;0+0+0;0+0+0
|
Japan;United Kingdom
|
|||||
04512
|
A General Offline Reinforcement Learning Framework for Interactive Recommendation
|
main
|
Technical
|
This paper studies the problem of learning interactive recommender systems from logged feedbacks without any exploration in online environments. We address the problem by proposing a general offline reinforcement learning framework for recommendation, which enables maximizing cumulative user rewards without online exploration. Specifically, we first introduce a probabilistic generative model for interactive recommendation, and then propose an effective inference algorithm for discrete and stochastic policy learning based on logged feedbacks. In order to perform offline learning more effectively, we propose five approaches to minimize the distribution mismatch between the logging policy and recommendation policy: support constraints, supervised regularization, policy constraints, dual constraints and reward extrapolation. We conduct extensive experiments on two public real-world datasets, demonstrating that the proposed methods can achieve superior performance over existing supervised learning and reinforcement learning methods for recommendation.
|
Data Mining and Knowledge Management
|
Teng Xiao; Donglin Wang
|
Machine Intelligence Lab (MiLAB), AI Division, School of Engineering, Westlake University; Machine Intelligence Lab (MiLAB), AI Division, School of Engineering, Westlake University
|
https://cdn.aaai.org/ojs/16579/16579-13-20073-1-2-20210518.pdf
|
https://aaai.org/papers/04512-a-general-offline-reinforcement-learning-framework-for-interactive-recommendation/
|
10.1609/aaai.v35i5.16579
| 191,359 | 86 |
https://scholar.google.com/scholar?cites=5204357080640185717&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
gmail.com;westlake.edu.cn
|
gmail.com;westlake.edu.cn
| 2 |
0;0
|
Westlake University
|
School of Engineering
|
https://www.westlake.edu.cn
|
Westlake
|
0;0
|
China
|
||||||
06185
|
A General Setting for Gradual Semantics Dealing with Similarity
|
main
|
Technical
|
The paper discusses theoretical foundations that describe principles and processes involved in defining semantics that deal with similarity between arguments. Such semantics compute the strength of an argument on the basis of the strengths of its attackers, similarities between those attackers, and an initial weight ascribed to the argument. We define a semantics by three functions: an adjustment function that updates the strengths of attackers on the basis of their similarities, an aggregation function that computes the strength of the group of attackers, and an influence function that evaluates the impact of the group on the argument's initial weight. We propose intuitive constraints for the three functions and key rationality principles for semantics, and show how the former lead to the satisfaction of the latter. Then, we propose a broad family of semantics whose instances satisfy the principles. Finally, we analyse the existing adjustment functions and show that they violate some properties, then we propose novel ones and use them for generalizing h-Categorizer.
|
Knowledge Representation and Reasoning
|
Leila Amgoud; Victor David
|
IRIT–CNRS–ANITI, Toulouse University, France; IRIT–CNRS, Toulouse University, France
|
https://cdn.aaai.org/ojs/16769/16769-13-20263-1-2-20210518.pdf
|
https://aaai.org/papers/06185-a-general-setting-for-gradual-semantics-dealing-with-similarity/
|
10.1609/aaai.v35i7.16769
| 223,608 | 14 |
https://scholar.google.com/scholar?cites=5692192916511333021&as_sdt=2005&sciodt=0,5&hl=en
| 8 |
irit.fr;irit.fr
|
irit.fr;irit.fr
| 2 |
0;0
|
Toulouse University
|
IRIT–CNRS–ANITI
|
https://www.univ-toulouse.fr
|
UT
|
0;0
|
Toulouse
|
0;0
|
France
|
||||
12104
|
A Generative Adversarial Framework for Bounding Confounded Causal Effects
|
main
|
Technical
|
Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl's structural causal model. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, using an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution, and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make assumption about the type of structural equations and variables. Experiments using both synthetic and real-world datasets are conducted.
|
Reasoning under Uncertainty
|
Yaowei Hu; Yongkai Wu; Lu Zhang; Xintao Wu
|
University of Arkansas; Clemson University; University of Arkansas; University of Arkansas
|
https://cdn.aaai.org/ojs/17437/17437-13-20931-1-2-20210518.pdf
|
https://aaai.org/papers/12104-a-generative-adversarial-framework-for-bounding-confounded-causal-effects/
|
10.1609/aaai.v35i13.17437
| 563,909 | 38 |
https://scholar.google.com/scholar?cites=5156922859419669239&as_sdt=40005&sciodt=0,10&hl=en
| 6 |
uark.edu;clemson.edu;uark.edu;uark.edu
|
uark.edu;clemson.edu;uark.edu;uark.edu
| 4 |
0;1;0;0
|
University of Arkansas;Clemson University
|
;
|
https://www.uark.edu;https://www.clemson.edu
|
UARK;Clemson
|
0;0;0;0
|
United States
|
||||||
02260
|
A Global Occlusion-Aware Approach to Self-Supervised Monocular Visual Odometry
|
main
|
Technical
|
Self-Supervised monocular visual odometry (VO) is often cast into a view synthesis problem based on depth and camera pose estimation. One of the key challenges is to accurately and robustly estimate depth with occlusions and moving objects in the scene. Existing methods simply detect and mask out regions of occlusions locally by several convolutional layers, and then perform only partial view synthesis in the rest of the image. However, occlusion and moving object detection is an unsolved problem itself which requires global layout information. Inaccurate detection inevitably results in incorrect depth as well as pose estimation. In this work, instead of locally detecting and masking out occlusions and moving objects, we propose to alleviate their negative effects on monocular VO implicitly but more effectively from two global perspectives. First, a multi-scale non-local attention module, consisting of both intra-stage augmented attention and cascaded across-stage attention, is proposed for robust depth estimation given occlusions, alleviating the impacts of occlusions via global attention modeling. Second, adversarial learning is introduced in view synthesis for monocular VO. Unlike existing methods that use pixel-level losses on the quality of synthesized views, we enforce the synthetic view to be indistinguishable from the real one at the scene-level. Such a global constraint again helps cope with occluded and moving regions. Extensive experiments on the KITTI dataset show that our approach achieves new state-of-the-art in both pose estimation and depth recovery.
|
Computer Vision II
|
Yao Lu; Xiaoli Xu; Mingyu Ding; Zhiwu Lu; Tao Xiang
|
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China+Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China+Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China; The University of Hong Kong, Pokfulam, Hong Kong, China; Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China+Beijing Key Laboratory of Big Data Management and Analysis Methods, Beijing 100872, China; University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom
|
https://cdn.aaai.org/ojs/16325/16325-13-19819-1-2-20210518.pdf
|
https://aaai.org/papers/02260-a-global-occlusion-aware-approach-to-self-supervised-monocular-visual-odometry/
|
10.1609/aaai.v35i3.16325
| 1,132,154 | 6 |
https://scholar.google.com/scholar?cites=14597038271100492284&as_sdt=5,34&sciodt=0,34&hl=en
| 5 |
ruc.edu.cn; ; ;ruc.edu.cn;surrey.ac.uk
|
ruc.edu.cn; ; ;ruc.edu.cn;surrey.ac.uk
| 5 |
0+1;0+1;2;0+1;3
|
Renmin University of China;Beijing Key Laboratory of Big Data Management and Analysis Methods;The University of Hong Kong;University of Surrey
|
Gaoling School of Artificial Intelligence;Big Data Management and Analysis;;
|
http://www.ruc.edu.cn;;https://www.hku.hk;https://www.surrey.ac.uk
|
RUC;;HKU;Surrey
|
0+0;0+0;1;0+0;2
|
Beijing;Pokfulam;Guildford
|
0+0;0+0;0;0+0;1
|
China;United Kingdom
|
||||
13433
|
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
|
main
|
Technical
|
We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by calculating the matching score based on learned features, leading to insufficient model reasoning ability. In this paper, we propose a graph- reasoning network (GRN) to address the problem. GRN first conducts pre-training based on ALBERT using next utterance prediction and utterance order prediction tasks specifically devised for response selection. These two customized pre-training tasks can endow our model with the ability of capturing semantical and chronological dependency between utterances. We then fine-tune the model on an integrated network with sequence reasoning and graph reasoning structures. The sequence reasoning module conducts inference based on the highly summarized context vector of utterance-response pairs from the global perspective. The graph reasoning module conducts the reasoning on the utterance-level graph neural network from the local perspective. Experiments on two conversational reasoning datasets show that our model can dramatically outperform the strong baseline methods and can achieve performance which is close to human-level.
|
Speech and Natural Language Processing II
|
Yongkang Liu; Shi Feng; Daling Wang; Kaisong Song; Feiliang Ren; Yifei Zhang
|
Northeastern University; Northeastern University; Northeastern University+Alibaba Group; Alibaba Group; Northeastern University; Northeastern University
|
https://cdn.aaai.org/ojs/17585/17585-13-21079-1-2-20210518.pdf
|
https://aaai.org/papers/13433-a-graph-reasoning-network-for-multi-turn-response-selection-via-customized-pre-training/
|
10.1609/aaai.v35i15.17585
| 385,110 | 19 |
https://scholar.google.com/scholar?cites=96339012693560200&as_sdt=5,33&sciodt=0,33&hl=en
| 4 |
163.com; ffengshi;cse.neu.edu.cn;alibaba-inc.com;cse.neu.edu.cn;cse.neu.edu.cn
|
163.com; ffengshi;cse.neu.edu.cn;alibaba-inc.com;cse.neu.edu.cn;cse.neu.edu.cn
| 6 |
0;0;0+1;1;0;0
|
Northeastern University;Alibaba Group
|
;
|
https://www.northeastern.edu;https://www.alibaba.com
|
NEU;Alibaba
|
0;0;0+1;1;0;0
|
United States;China
|
||||||
04688
|
A Graph-based Relevance Matching Model for Ad-hoc Retrieval
|
main
|
Technical
|
To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial. Recent deep learning models regard the task as a term-level matching problem, which seeks exact or similar query patterns in the document. However, we argue that they are inherently based on local interactions and do not generalise to ubiquitous, non-consecutive contextual relationships. In this work, we propose a novel relevance matching model based on graph neural networks to leverage the document-level word relationships for ad-hoc retrieval. In addition to the local interactions, we explicitly incorporate all contexts of a term through the graph-of-word text format. Matching patterns can be revealed accordingly to provide a more accurate relevance score. Our approach significantly outperforms strong baselines on two ad-hoc benchmarks. We also experimentally compare our model with BERT and show our advantages on long documents.
|
Data Mining and Knowledge Management
|
Yufeng Zhang; Jinghao Zhang; Zeyu Cui; Shu Wu; Liang Wang
|
Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences; Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences + School of Artificial Intelligence, University of Chinese Academy of Sciences; Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences + School of Artificial Intelligence, University of Chinese Academy of Sciences; Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences + School of Artificial Intelligence, University of Chinese Academy of Sciences + Artificial Intelligence Research, Chinese Academy of Sciences; Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences + School of Artificial Intelligence, University of Chinese Academy of Sciences
|
https://cdn.aaai.org/ojs/16599/16599-13-20093-1-2-20210518.pdf
|
https://aaai.org/papers/04688-a-graph-based-relevance-matching-model-for-ad-hoc-retrieval/
|
10.1609/aaai.v35i5.16599
| 2,743,405 | 25 |
https://scholar.google.com/scholar?cites=2746327770639979761&as_sdt=5,44&sciodt=0,44&hl=en
| 6 |
cripac.ia.ac.cn;cripac.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn
|
cripac.ia.ac.cn;cripac.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn;nlpr.ia.ac.cn
| 5 |
0;0+1;0+1;0+1+0;0+1
|
Chinese Academy of Sciences;University of Chinese Academy of Sciences
|
Institute of Automation;School of Artificial Intelligence
|
http://www.ia.cas.cn;http://www.ucas.ac.cn
|
CAS;UCAS
|
;;;
|
0;0+0;0+0;0+0+0;0+0
|
China
|
|||||
00591
|
A Hierarchical Approach to Multi-Event Survival Analysis
|
main
|
Technical
|
In multi-event survival analysis, one aims to predict the probability of multiple different events occurring over some time horizon. One typically assumes that the timing of events is drawn from some distribution conditioned on an individual's covariates. However, during training, one does not have access to this distribution, and the natural variation in the observed event times makes the task of survival prediction challenging, on top of the potential interdependence among events. To address this issue, we introduce a novel approach for multi-event survival analysis that models the probability of event occurrence hierarchically at different time scales, using coarse predictions (e.g., monthly predictions) to iteratively guide predictions at finer and finer grained time scales (e.g., daily predictions). We evaluate the proposed approach across several publicly available datasets in terms of both intra-event, inter-individual (global) and intra-individual, inter-event (local) consistency. We show that the proposed method consistently outperforms well-accepted and commonly used approaches to multi-event survival analysis. When estimating survival curves for Alzheimer's disease and mortality, our approach achieves a C-index of 0.91 (95% CI 0.88-0.93) and a local consistency score of 0.97 (95% CI 0.94-0.98) compared to a C-index of 0.75 (95% CI 0.70-0.80) and a local consistency score of 0.94 (95% CI 0.91-0.97) when modeling each event separately. Overall, our approach improves the accuracy of survival predictions by iteratively reducing the original task to a set of nested, simpler subtasks.
|
Application Domains
|
Donna Tjandra; Yifei He; Jenna Wiens
|
Computer Science and Engineering, University of Michigan, Ann Arbor MI, USA; Computer Science and Engineering, University of Michigan, Ann Arbor MI, USA; Computer Science and Engineering, University of Michigan, Ann Arbor MI, USA
|
https://cdn.aaai.org/ojs/16138/16138-13-19632-1-2-20210518.pdf
|
https://aaai.org/papers/00591-a-hierarchical-approach-to-multi-event-survival-analysis/
|
10.1609/aaai.v35i1.16138
| 381,251 | 18 |
https://scholar.google.com/scholar?cites=2326263437783348710&as_sdt=5,33&sciodt=0,33&hl=en
| 5 |
umich.edu;umich.edu;umich.edu
|
umich.edu;umich.edu;umich.edu
|
http://adni.loni.usc.edu/wp-content/uploads/how toapply/ADNI Acknowledgement List.pdf
| 3 |
0;0;0
|
University of Michigan
|
Computer Science and Engineering
|
https://www.umich.edu
|
UM
|
0;0;0
|
Ann Arbor
|
0;0;0
|
United States
|
|||
01637
|
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
|
main
|
Technical
|
Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video-level supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end frame of each action category in a video. However, the existing MIL-based approach has a major limitation of only capturing the most discriminative frames of an action, ignoring the full extent of an activity. Moreover, these methods cannot model background activity effectively, which plays an important role in localizing foreground activities. In this paper, we present a novel framework named HAM-Net with a hybrid attention mechanism which includes temporal soft, semi-soft and hard attentions to address these issues. Our temporal soft attention module, guided by an auxiliary background class in the classification module, models the background activity by introducing an ``action-ness'' score for each video snippet. Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary. Our proposed approach outperforms recent state-of-the-art methods by at least 2.2% mAP at IoU threshold 0.5 on the THUMOS14 dataset, and by at least 1.3% mAP at IoU threshold 0.75 on the ActivityNet1.2 dataset.
|
Computer Vision I
|
Ashraful Islam; Chengjiang Long; Richard Radke
|
Rensselaer Polytechnic Institute; JD Digits AI Lab; Rensselaer Polytechnic Institute
|
https://cdn.aaai.org/ojs/16256/16256-13-19750-1-2-20210518.pdf
|
https://aaai.org/papers/01637-a-hybrid-attention-mechanism-for-weakly-supervised-temporal-action-localization/
|
10.1609/aaai.v35i2.16256
| 1,936,239 | 144 |
https://scholar.google.com/scholar?cites=8206551382397542190&as_sdt=5,44&sciodt=0,44&hl=en
| 11 |
rpi.edu;jd.com;ecse.rpi.edu
|
rpi.edu;jd.com;ecse.rpi.edu
| 3 |
0;1;0
|
Rensselaer Polytechnic Institute;JD Digits
|
;AI Lab
|
https://www.rpi.edu;https://digits.jd.com
|
RPI;JD Digits
|
0;1;0
|
United States;China
|
||||||
04036
|
A Hybrid Bandit Framework for Diversified Recommendation
|
main
|
Technical
|
The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set. However, the investigation of users' personalized preferences on the diversity properties of an item set is usually ignored. To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. Specifically, LMDB employs modular functions to model the relevance properties of each item, and dispersion functions to describe the diversity properties of an item set. Moreover, we also develop a learning algorithm, called Linear Modular Dispersion Hybrid (LMDH) to solve the LMDB problem and derive a gap-free bound on its n-step regret. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity.
|
Data Mining and Knowledge Management
|
Qinxu Ding; Yong Liu; Chunyan Miao; Fei Cheng; Haihong Tang
|
Alibaba-NTU Singapore Joint Research Institute; Alibaba-NTU Singapore Joint Research Institute + Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY); School of Computer Science and Engineering, Nanyang Technological University; Alibaba Group; Alibaba Group
|
https://cdn.aaai.org/ojs/16524/16524-13-20018-1-2-20210518.pdf
|
https://aaai.org/papers/04036-a-hybrid-bandit-framework-for-diversified-recommendation/
|
10.1609/aaai.v35i5.16524
| 272,109 | 29 |
https://scholar.google.com/scholar?cites=4743029495488784588&as_sdt=5,33&sciodt=0,33&hl=en
| 6 |
e.ntu.edu.sg;ntu.edu.sg;ntu.edu.sg;alibaba-inc.com;taobao.com
|
e.ntu.edu.sg;ntu.edu.sg;ntu.edu.sg;alibaba-inc.com;taobao.com
| 5 |
0;0+1;1;2;2
|
Alibaba-NTU Singapore Joint Research Institute;Nanyang Technological University;Alibaba Group
|
;Research Centre of Excellence in Active Living for the Elderly;
|
https://www.antri.sg;https://www.ntu.edu.sg;https://www.alibaba.com
|
ANSJRI;NTU;Alibaba
|
0;0+0;0;1;1
|
Singapore;China
|
||||||
04366
|
A Hybrid Probabilistic Approach for Table Understanding
|
main
|
Technical
|
Tables of data are used to record vast amounts of socioeconomic, scientific, and governmental information. Although humans create tables using underlying organizational principles, unfortunately AI systems struggle to understand the contents of these tables. This paper introduces an end-to-end system for table understanding, the process of capturing the relational structure of data in tables. We introduce models that identify cell types, group these cells into blocks of data that serve a similar functional role, and predict the relationships between these blocks. We introduce a hybrid, neuro-symbolic approach, combining embedded representations learned from thousands of tables with probabilistic constraints that capture regularities in how humans organize tables. Our neuro-symbolic model is better able to capture positional invariants of headers and enforce homogeneity of data types. One limitation in this research area is the lack of rich datasets for evaluating end-to-end table understanding, so we introduce a new benchmark dataset comprised of 431 diverse tables from data.gov. The evaluation results show that our system achieves the state-of-the-art performance on cell type classification, block identification, and relationship prediction, improving over prior efforts by up to 7% of macro F1 score.
|
Data Mining and Knowledge Management
|
Kexuan Sun; Harsha Rayudu; Jay Pujara
|
University of Southern California, Information Sciences Institute; University of Southern California, Information Sciences Institute; University of Southern California, Information Sciences Institute
|
https://cdn.aaai.org/ojs/16562/16562-13-20056-1-2-20210518.pdf
|
https://aaai.org/papers/04366-a-hybrid-probabilistic-approach-for-table-understanding/
|
10.1609/aaai.v35i5.16562
| 630,295 | 19 |
https://scholar.google.com/scholar?cites=292928967195802939&as_sdt=5,31&sciodt=0,31&hl=en
| 5 |
usc.edu;usc.edu;isi.edu
|
usc.edu;usc.edu;isi.edu
| 3 |
0;0;0
|
University of Southern California
|
Information Sciences Institute
|
https://www.usc.edu
|
USC
|
0;0;0
|
United States
|
||||||
10842
|
A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method
|
main
|
Technical
|
Recent theoretical analyses reveal that existing Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods need large mini-batches of samples (exponentially dependent on the dimension) to reduce the mean square error of gradient estimates and ensure non-asymptotic convergence guarantees when the target distribution has a nonconvex potential function. In this paper, we propose a novel SG-MCMC algorithm, called Hybrid Stochastic Gradient Hamiltonian Monte Carlo (HSG-HMC) method, which needs merely one sample per iteration and possesses a simple structure with only one hyperparameter. Such improvement leverages a hybrid stochastic gradient estimator that exploits historical stochastic gradient information to control the mean square error. Theoretical analyses show that our method obtains the best-known overall sample complexity to achieve epsilon-accuracy in terms of the 2-Wasserstein distance for sampling from distributions with nonconvex potential functions. Empirical studies on both simulated and real-world datasets demonstrate the advantage of our method.
|
Machine Learning V
|
Chao Zhang; Zhijian Li; Zebang Shen; Jiahao Xie; Hui Qian
|
Zhejiang University; Zhejiang University; University of Pennsylvania; Zhejiang University; Zhejiang University
|
https://cdn.aaai.org/ojs/17295/17295-13-20789-1-2-20210518.pdf
|
https://aaai.org/papers/10842-a-hybrid-stochastic-gradient-hamiltonian-monte-carlo-method/
|
10.1609/aaai.v35i12.17295
| 1,219,075 | 3 |
https://scholar.google.com/scholar?cites=18059679541690339489&as_sdt=80000005&sciodt=0,23&hl=en
| 3 |
zju.edu.cn;zju.edu.cn;seas.upenn.edu;zju.edu.cn;zju.edu.cn
|
zju.edu.cn;zju.edu.cn;seas.upenn.edu;zju.edu.cn;zju.edu.cn
| 5 |
0;0;1;0;0
|
Zhejiang University;University of Pennsylvania
|
;
|
https://www.zju.edu.cn;https://www.upenn.edu
|
ZJU;UPenn
|
0;0;1;0;0
|
China;United States
|
||||||
13543
|
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis
|
main
|
Technical
|
Aspect based sentiment analysis (ABSA) involves three fundamental subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Early works only focused on solving one of these subtasks individually. Some recent work focused on solving a combination of two subtasks, e.g., extracting aspect terms along with sentiment polarities or extracting the aspect and opinion terms pair-wisely. More recently, the triple extraction task has been proposed, i.e., extracting the (aspect term, opinion term, sentiment polarity) triples from a sentence. However, previous approaches fail to solve all subtasks in a unified end-to-end framework. In this paper, we propose a complete solution for ABSA. We construct two machine reading comprehension (MRC) problems, and solve all subtasks by joint training two BERT-MRC models with parameters sharing. We conduct experiments on these subtasks and results on several benchmark datasets demonstrate the effectiveness of our proposed framework, which significantly outperforms existing state-of-the-art methods.
|
Speech and Natural Language Processing II
|
Yue Mao; Yi Shen; Chao Yu; Longjun Cai
|
Alibaba Group, Beijing, China; Alibaba Group, Beijing, China; Alibaba Group, Beijing, China; Alibaba Group, Beijing, China
|
https://cdn.aaai.org/ojs/17597/17597-13-21091-1-2-20210518.pdf
|
https://aaai.org/papers/13543-a-joint-training-dual-mrc-framework-for-aspect-based-sentiment-analysis/
|
10.1609/aaai.v35i15.17597
| 1,307,726 | 253 |
https://scholar.google.com/scholar?cites=4150219614153666538&as_sdt=2005&sciodt=0,5&hl=en
| 6 |
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com
|
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com
| 4 |
0;0;0;0
|
Alibaba Group
|
https://www.alibaba.com
|
Alibaba
|
0;0;0;0
|
Beijing
|
0;0;0;0
|
China
|
|||||
12657
|
A Lightweight Neural Model for Biomedical Entity Linking
|
main
|
Technical
|
Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods have advanced the state-of-the-art by allowing for rich representations of word sequences. However, they often have hundreds of millions of parameters and require heavy computing resources, which limits their applications in resource-limited scenarios. Here, we propose a lightweight neural method for biomedical entity linking, which needs just a fraction of the parameters of a BERT model and much less computing resources. Our method uses a simple alignment layer with attention mechanisms to capture the variations between mention and entity names. Yet, we show that our model is competitive with previous work on standard evaluation benchmarks.
|
Speech and Natural Language Processing I
|
Lihu Chen; Gaël Varoquaux; Fabian M. Suchanek
|
LTCI & T ´el´ecom Paris & Institut Polytechnique de Paris, France; Inria & CEA & Universit ´e Paris-Saclay, France; LTCI & T ´el´ecom Paris & Institut Polytechnique de Paris, France
|
https://cdn.aaai.org/ojs/17499/17499-13-20993-1-2-20210518.pdf
|
https://aaai.org/papers/12657-a-lightweight-neural-model-for-biomedical-entity-linking/
|
10.1609/aaai.v35i14.17499
| 1,150,267 | 39 |
https://scholar.google.com/scholar?cites=390967305667106151&as_sdt=40005&sciodt=0,10&hl=en
| 15 |
telecom-paris.fr;inria.fr;telecom-paris.fr
|
telecom-paris.fr;inria.fr;telecom-paris.fr
| 3 |
0;1;0
|
Télécom Paris;Inria
|
LTCI;
|
https://www.telecom-paris.fr;https://www.inria.fr
|
Télécom Paris;Inria
|
0;0;0
|
France
|
||||||
04776
|
A Market-Inspired Bidding Scheme for Peer Review Paper Assignment
|
AAAI Technical Track Focus Area
|
Technical
|
We propose a market-inspired bidding scheme for the assignment of paper reviews in large academic conferences. We provide an analysis of the incentives of reviewers during the bidding phase, when reviewers have both private costs and some information about the demand for each paper; and their goal is to obtain the best possible k papers for a predetermined k. We show that by assigning `budgets' to reviewers and a `price' for every paper that is (roughly) proportional to its demand, the best response of a reviewer is to bid sincerely, i.e., on her most favorite papers, and match the budget even when it is not enforced. This game-theoretic analysis is based on a simple, prototypical assignment algorithm. We show via extensive simulations on bidding data from real conferences, that our bidding scheme would substantially improve both the bid distribution and the resulting assignment.
|
AI for Conference Organization and Delivery
|
Reshef Meir; Jérôme Lang; Julien Lesca; Nicholas Mattei; Natan Kaminsky
|
Technion—Israel Institute of Technology; Université Paris Dauphine; Université Paris Dauphine; Tulane University; Technion—Israel Institute of Technology
|
https://cdn.aaai.org/ojs/16609/16609-13-20103-1-2-20210518.pdf
|
https://aaai.org/papers/04776-a-market-inspired-bidding-scheme-for-peer-review-paper-assignment/
|
10.1609/aaai.v35i6.16609
| 552,730 | 35 |
https://scholar.google.com/scholar?cites=4298568262062648383&as_sdt=2005&sciodt=0,5&hl=en
| 7 |
technion.ac.il;lamsade.dauphine.fr;lamsade.dauphine.fr;tulane.edu;campus.technion.ac.il
|
technion.ac.il;lamsade.dauphine.fr;lamsade.dauphine.fr;tulane.edu;campus.technion.ac.il
| 5 |
0;1;1;2;0
|
Technion—Israel Institute of Technology;Université Paris Dauphine;Tulane University
|
;;
|
https://www.technion.ac.il/en/;https://www.univ-paris-dauphine.fr;https://www.tulane.edu
|
Technion;UPD;Tulane
|
0;1;1;2;0
|
Israel;France;United States
|
||||||
05760
|
A Model of Winners Allocation
|
main
|
Technical
|
We propose a model of winners allocation. In this model, we are given are two elections where the sets of candidates may intersect. The goal is to find two disjoint winning committees from respectively the two elections that are subjected to certain reasonable restrictions. For our model, we first propose several desirable properties. Then, we investigate the implication relationships among these properties. Finally, we study the complexity of computing winners allocations providing these properties. For hardness results, we also study some fixed-parameter algorithms.
|
Game Theory and Economic Paradigms
|
Yongjie Yang
|
Chair of Economic Theory, Saarland University, Saarbrucken, Germany
|
https://cdn.aaai.org/ojs/16722/16722-13-20216-1-2-20210518.pdf
|
https://aaai.org/papers/05760-a-model-of-winners-allocation/
|
10.1609/aaai.v35i6.16722
| 155,861 | 2 |
https://scholar.google.com/scholar?cites=14655985434935210729&as_sdt=5,33&sciodt=0,33&hl=en
| 4 |
gmail.com
|
gmail.com
| 1 |
0
|
Saarland University
|
Chair of Economic Theory
|
https://www.uni-saarland.de
|
0
|
Saarbrucken
|
0
|
Germany
|
|||||
06948
|
A Multi-step-ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting
|
main
|
Technical
|
Predicting extreme weather events such as tropical and extratropical cyclones is of vital scientific and societal importance. Of late, machine learning methods have found their way to weather analysis and prediction, but mostly, these methods use machine learning merely as a complement to traditional numerical weather prediction models. Although some pure machine learning and data-driven approaches for weather prediction have been developed, they mainly formulate the problem similar to pattern recognition or follow the train of thought of traditional time-series models for extreme weather event forecasting; for the former, this usually yields only single-step ahead prediction, and for the latter, this lacks the flexibility to account for observed weather features as such methods concern only the patterns of the extreme weather occurrences. In this paper, we depart from the typical practice of pattern recognition and time-series approaches and focus on employing machine learning to estimate the probabilities of extreme weather occurrences in a multi-step-ahead (MSA) fashion given information on both weather features and the realized occurrences of extreme weather. Specifically, we propose a Markov conditional forward (MCF) model that adopts the Markov property between the occurrences of extreme weather for MSA extreme weather forecasting. Moreover, for better long-term prediction, we propose three novel cube perturbation methods to address error accumulation in our model. Experimental results on a real-world extreme weather dataset show the superiority of the proposed MCF model in terms of prediction accuracy for both short-term and long-term forecasting; moreover, the three cube perturbation methods successfully increase the fault tolerance and generalization ability of the MCF model, yielding significant improvements for long-term prediction.
|
Machine Learning I
|
Chia-Yuan Chang; Cheng-Wei Lu; Chuan-Ju Wang
|
Research Center for Information Technology Innovation, Academia Sinica, Taiwan; Research Center for Information Technology Innovation, Academia Sinica, Taiwan; Research Center for Information Technology Innovation, Academia Sinica, Taiwan
|
https://cdn.aaai.org/ojs/16856/16856-13-20350-1-2-20210518.pdf
|
https://aaai.org/papers/06948-a-multi-step-ahead-markov-conditional-forward-model-with-cube-perturbations-for-extreme-weather-forecasting/
|
10.1609/aaai.v35i8.16856
| 1,218,750 | 7 |
https://scholar.google.com/scholar?cites=2977529520424232619&as_sdt=80000005&sciodt=0,23&hl=en
| 6 |
citi.sinica.edu.tw;citi.sinica.edu.tw;citi.sinica.edu.tw
|
citi.sinica.edu.tw;citi.sinica.edu.tw;citi.sinica.edu.tw
| 3 |
0;0;0
|
Academia Sinica
|
Research Center for Information Technology Innovation
|
https://www.sinica.edu.tw
|
AS
|
0;0;0
|
Taiwan, China
|
||||||
11755
|
A Multivariate Complexity Analysis of the Material Consumption Scheduling Problem
|
main
|
Technical
|
The NP-hard Material Consumption Scheduling Problem and closely related problems have been thoroughly studied since the 1980's. Roughly speaking, the problem deals with minimizing the makespan when scheduling jobs that consume non-renewable resources. We focus on the single-machine case without preemption: from time to time, the resources of the machine are (partially) replenished, thus allowing for meeting a necessary pre-condition for processing further jobs, each of which having individual resource demands. We initiate a systematic exploration of the parameterized computational complexity landscape of the problem, providing parameterized tractability as well as intractability results. Doing so, we mainly investigate how parameters related to the resource supplies influence the computational complexity. Thereby, we get a deepened understanding of this fundamental scheduling problem.
|
Planning, Routing, and Scheduling
|
Matthias Bentert; Robert Bredereck; Péter Györgyi; Andrzej Kaczmarczyk; Rolf Niedermeier
|
Technische Universit ¨at Berlin, Faculty IV , Algorithmics and Computational Complexity, Berlin, Germany; Technische Universit ¨at Berlin, Faculty IV , Algorithmics and Computational Complexity, Berlin, Germany + Humboldt-Universit ¨at zu Berlin, Institut f ¨ur Informatik, Algorithm Engineering, Berlin, Germany; Institute for Computer Science and Control, Budapest, Hungary; Technische Universit ¨at Berlin, Faculty IV , Algorithmics and Computational Complexity, Berlin, Germany; Technische Universit ¨at Berlin, Faculty IV , Algorithmics and Computational Complexity, Berlin, Germany
|
https://cdn.aaai.org/ojs/17397/17397-13-20891-1-2-20210518.pdf
|
https://aaai.org/papers/11755-a-multivariate-complexity-analysis-of-the-material-consumption-scheduling-problem/
|
10.1609/aaai.v35i13.17397
| 161,276 | 3 |
https://scholar.google.com/scholar?cites=5403149486490943241&as_sdt=5,44&sciodt=0,44&hl=en
| 19 |
tu-berlin.de;hu-berlin.de;sztaki.hu;tu-berlin.de;tu-berlin.de
|
tu-berlin.de;hu-berlin.de;sztaki.hu;tu-berlin.de;tu-berlin.de
| 5 |
0;0+1;2;0;0
|
Technische Universität Berlin;Humboldt-Universität zu Berlin;Institute for Computer Science and Control
|
Faculty IV, Algorithmics and Computational Complexity;Institut für Informatik, Algorithm Engineering;
|
https://www.tu-berlin.de;https://www.hu-berlin.de;
|
TU Berlin;HU Berlin;
|
0;0+0;0;0
|
Berlin;
|
0;0+0;1;0;0
|
Germany;Hungary
|
||||
14594
|
A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness
|
main
|
Technical
|
Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where the content of some users is limited and the model fails to capture discriminative features of users. To address this issue, we hypothesize that users could be grouped together based on their rating biases as well as degree of rating consistency and the knowledge learned from groups could be employed to analyze the users with limited data. Therefore, in this paper, a neural group-wise sentiment analysis model with data sparsity awareness is proposed. The user-centred document representations are generated by incorporating a group-based user encoder. Furthermore, a multi-task learning framework is employed to jointly modelusers’ rating biases and their degree of rating consistency. One task is vanilla populationlevel sentiment analysis and the other is groupwise sentiment analysis. Experimental results on three real-world datasets show that the proposed approach outperforms some state-of the-art methods. Moreover, model analysis and case study demonstrate its effectiveness of modeling user rating biases and variances.
|
Speech and Natural Language Processing III
|
Deyu Zhou; Meng Zhang; Linhai Zhang; Yulan He
|
School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China; Department of Computer Science, University of Warwick, UK
|
https://cdn.aaai.org/ojs/17715/17715-13-21209-1-2-20210518.pdf
|
https://aaai.org/papers/14594-a-neural-group-wise-sentiment-analysis-model-with-data-sparsity-awareness/
|
10.1609/aaai.v35i16.17715
| 975,805 | 14 |
https://scholar.google.com/scholar?cites=3671318569415888004&as_sdt=5,44&sciodt=0,44&hl=en
| 7 |
seu.edu.cn;seu.edu.cn;seu.edu.cn;warwick.ac.uk
|
seu.edu.cn;seu.edu.cn;seu.edu.cn;warwick.ac.uk
| 4 |
0;0;0;1
|
Southeast University;University of Warwick
|
School of Computer Science and Engineering;Department of Computer Science
|
https://www.seu.edu.cn/;https://warwick.ac.uk
|
SEU;Warwick
|
0;0;0;1
|
China;United Kingdom
|
||||||
12158
|
A New Bounding Scheme for Influence Diagrams
|
main
|
Technical
|
Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision variables. Computing the maximum expected utility (MEU) and the optimizing policy is exponential in the constrained induced width and therefore is notoriously difficult for larger models. In this paper, we develop a new bounding scheme for MEU that applies partitioning based approximations on top of the decomposition scheme called a multi-operator cluster DAG for influence diagrams that is more sensitive to the underlying structure of the model than the classical join-tree decomposition of influence diagrams. Our bounding scheme utilizes a cost-shifting mechanism to tighten the bound further. We demonstrate the effectiveness of the proposed scheme on various hard benchmarks.
|
Reasoning under Uncertainty
|
Radu Marinescu; Junkyu Lee; Rina Dechter
|
IBM Research Europe; University of California Irvine; University of California Irvine
|
https://cdn.aaai.org/ojs/17443/17443-13-20937-1-2-20210518.pdf
|
https://aaai.org/papers/12158-a-new-bounding-scheme-for-influence-diagrams/
|
10.1609/aaai.v35i13.17443
| 575,700 | 2 |
https://scholar.google.com/scholar?cites=193003005234135326&as_sdt=2005&sciodt=0,5&hl=en
| 11 |
ie.ibm.com;uci.edu;ics.uci.edu
|
ie.ibm.com;uci.edu;ics.uci.edu
| 3 |
0;1;1
|
IBM Research;University of California, Irvine
|
Research;
|
https://www.ibm.com/research/europe;https://www.uci.edu
|
IBM Research;UCI
|
1;1
|
;Irvine
|
0;1;1
|
Europe;United States
|
||||
03377
|
A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation
|
main
|
Technical
|
Interpretability has been regarded as an essential component for deploying deep neural networks, in which the saliency-based method is one of the most prevailing interpretable approaches since it can generate individually intuitive heatmaps that highlight parts of the input image that are most important to the decision of the deep networks on a particular classification target. However, heatmaps generated by existing methods either contain little information to represent objects (perturbation-based methods) or cannot effectively locate multi-class objects (activation-based approaches). To address this issue, a two-stage framework for visualizing the interpretability of deep neural networks, called Activation Optimized with Perturbation (AOP), is designed to optimize activation maps generated by general activation-based methods with the help of perturbation-based methods. Finally, in order to obtain better explanations for different types of images, we further present an instance of the AOP framework, Smooth Integrated Gradient-based Class Activation Map (SIGCAM), which proposes a weighted GradCAM by applying the feature map as weight coefficients and employs I-GOS to optimize the base-mask generated by weighted GradCAM. Experimental results on common-used benchmarks, including deletion and insertion tests on ImageNet-1k, and pointing game tests on COCO2017, show that the proposed AOP and SIGCAM outperform the current state-of-the-art methods significantly by generating higher quality image-based saliency maps.
|
Computer Vision III
|
Qinglong Zhang; Lu Rao; Yubin Yang
|
State Key Laboratory for Novel Software Technology at Nanjing University; State Key Laboratory for Novel Software Technology at Nanjing University; State Key Laboratory for Novel Software Technology at Nanjing University
|
https://cdn.aaai.org/ojs/16450/16450-13-19944-1-2-20210518.pdf
|
https://aaai.org/papers/03377-a-novel-visual-interpretability-for-deep-neural-networks-by-optimizing-activation-maps-with-perturbation/
|
10.1609/aaai.v35i4.16450
| 6,842,240 | 33 |
https://scholar.google.com/scholar?cites=17728534838047108262&as_sdt=5,33&sciodt=0,33&hl=en
| 3 |
smail.nju.edu.cn;smail.nju.edu.cn;nju.edu.cn
|
smail.nju.edu.cn;smail.nju.edu.cn;nju.edu.cn
| 3 |
0;0;0
|
Nanjing University
|
State Key Laboratory for Novel Software Technology
|
http://www.nju.edu.cn
|
NJU
|
0;0;0
|
China
|
||||||
04785
|
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences
|
AAAI Technical Track Focus Area
|
Technical
|
Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others. A surge in the number of submissions received by leading AI conferences has challenged the sustainability of the review process by increasing the burden on the pool of qualified reviewers which is growing at a much slower rate. In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. Specifically, we design a procedure for (i) recruiting reviewers from the population not typically covered by major conferences and (ii) guiding them through the reviewing pipeline. In conjunction with the ICML 2020 --- a large, top-tier machine learning conference --- we recruit a small set of reviewers through our procedure and compare their performance with the general population of ICML reviewers. Our experiment reveals that a combination of the recruiting and guiding mechanisms allows for a principled enhancement of the reviewer pool and results in reviews of superior quality compared to the conventional pool of reviews as evaluated by senior members of the program committee (meta-reviewers).
|
AI for Conference Organization and Delivery
|
Ivan Stelmakh; Nihar B. Shah; Aarti Singh; Hal Daumé III
|
School of Computer Science, Carnegie Mellon University; School of Computer Science, Carnegie Mellon University; School of Computer Science, Carnegie Mellon University; University of Maryland, College Park + Microsoft Research, New York
|
https://cdn.aaai.org/ojs/16610/16610-13-20104-1-2-20210518.pdf
|
https://aaai.org/papers/04785-a-novice-reviewer-experiment-to-address-scarcity-of-qualified-reviewers-in-large-conferences/
|
10.1609/aaai.v35i6.16610
| 2,924,796 | 41 |
https://scholar.google.com/scholar?cites=17803470703831937367&as_sdt=5,33&sciodt=0,33&hl=en
| 7 |
cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;hal3.name
|
cs.cmu.edu;cs.cmu.edu;cs.cmu.edu;hal3.name
| 4 |
0;0;0;1+2
|
Carnegie Mellon University;University of Maryland;Microsoft Research
|
School of Computer Science;;
|
https://www.cmu.edu;https://www/umd.edu;https://www.microsoft.com/en-us/research
|
CMU;UMD;MSR
|
0;0;0;1+2
|
Pittsburgh;College Park;New York
|
0;0;0;0+0
|
United States
|
||||
07254
|
A One-Size-Fits-All Solution to Conservative Bandit Problems
|
main
|
Technical
|
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a given baseline at any time. We propose a One-Size-Fits-All solution to CBPs and present its applications to three encompassed problems, i.e. conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, we focus on a sample-path constraint on the actually received reward, and achieve better theoretical guarantees (T-independent additive regrets instead of T-dependent) and empirical performance. Furthermore, we extend the results and consider a novel conservative mean-variance bandit problem (MV-CBP), which measures the learning performance with both the expected reward and variability. For this extended problem, we provide a novel algorithm with O(1/T) normalized additive regrets (T-independent in the cumulative form) and validate this result through empirical evaluation.
|
Machine Learning I
|
Yihan Du; Siwei Wang; Longbo Huang
|
Tsinghua University; Tsinghua University; Tsinghua University
|
https://cdn.aaai.org/ojs/16891/16891-13-20385-1-2-20210518.pdf
|
https://aaai.org/papers/07254-a-one-size-fits-all-solution-to-conservative-bandit-problems/
|
10.1609/aaai.v35i8.16891
| 5,681,077 | 7 |
https://scholar.google.com/scholar?cites=4889444821447405882&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn
|
mails.tsinghua.edu.cn;tsinghua.edu.cn;tsinghua.edu.cn
| 3 |
0;0;0
|
Tsinghua University
|
https://www.tsinghua.edu.cn
|
THU
|
0;0;0
|
China
|
|||||||
05664
|
A Permutation-Equivariant Neural Network Architecture For Auction Design
|
main
|
Technical
|
Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. Theoretical approaches to the problem have hit some limits in the past decades and analytical solutions are known for only a few simple settings. Computational approaches to the problem through the use of LPs have their own set of limitations. Building on the success of deep learning, a new approach was recently proposed by Duetting et al. (2019) in which the auction is modeled by a feed-forward neural network and the design problem is framed as a learning problem. The neural architectures used in that work are general purpose and do not take advantage of any of the symmetries the problem could present, such as permutation equivariance. In this work, we consider auction design problems that have permutation-equivariant symmetry and construct a neural architecture that is capable of perfectly recovering the permutation-equivariant optimal mechanism, which we show is not possible with the previous architecture. We demonstrate that permutation-equivariant architectures are not only capable of recovering previous results, they also have better generalization properties.
|
Game Theory and Economic Paradigms
|
Jad Rahme; Samy Jelassi; Joan Bruna; S. Matthew Weinberg
|
Princeton University; Princeton University + Courant Institute of Mathematical Sciences, New York University; Courant Institute of Mathematical Sciences, New York University + Center for Data Science, New York University; Princeton University
|
https://cdn.aaai.org/ojs/16711/16711-13-20205-1-2-20210518.pdf
|
https://aaai.org/papers/05664-a-permutation-equivariant-neural-network-architecture-for-auction-design/
|
10.1609/aaai.v35i6.16711
| 994,328 | 68 |
https://scholar.google.com/scholar?cites=8224579073628846202&as_sdt=2005&sciodt=0,5&hl=en
| 11 |
princeton.edu; ; ;
|
princeton.edu; ; ;
| 4 |
0;0+1;1+1;0
|
Princeton University;New York University
|
;Courant Institute of Mathematical Sciences
|
https://www.princeton.edu;https://www.courant.nyu.edu
|
Princeton;NYU
|
1;1+1
|
;New York
|
0;0+0;0+0;0
|
United States
|
||||
11160
|
A Primal-Dual Online Algorithm for Online Matching Problem in Dynamic Environments
|
main
|
Technical
|
Recently, the online matching problem has attracted much attention due to its wide application on real-world decision-making scenarios. In stationary environments, by adopting the stochastic user arrival model, existing methods are proposed to learn dual optimal prices and are shown to achieve a fast regret bound. However, the stochastic model is no longer a proper assumption when the environment is changing, leading to an optimistic method that may suffer poor performance. In this paper, we study the online matching problem in dynamic environments in which the dual optimal prices are allowed to vary over time. We bound the dynamic regret of online matching problem by the sum of two quantities, including a regret of online max-min problem and a dynamic regret of online convex optimization (OCO) problem. Then we propose a novel online approach named Primal-Dual Online Algorithm (PDOA) to minimize both quantities. In particular, PDOA adopts the primal-dual framework by optimizing dual prices with the online gradient descent (OGD) algorithm to eliminate the online max-min problem's regret. Moreover, it maintains a set of OGD experts and combines them via an expert-tracking algorithm, which gives a sublinear dynamic regret bound for the OCO problem. We show that PDOA achieves an O(K sqrt{T(1+P_T)}) dynamic regret where K is the number of resources, T is the number of iterations and P_T is the path-length of any potential dual price sequence that reflects the dynamic environment. Finally, experiments on real applications exhibit the superiority of our approach.
|
Machine Learning V
|
Yu-Hang Zhou; Peng Hu; Chen Liang; Huan Xu; Guangda Huzhang; Yinfu Feng; Qing Da; Xinshang Wang; An-Xiang Zeng
|
Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China; Alibaba Group, Hangzhou, China
|
https://cdn.aaai.org/ojs/17331/17331-13-20825-1-2-20210518.pdf
|
https://aaai.org/papers/11160-a-primal-dual-online-algorithm-for-online-matching-problem-in-dynamic-environments/
|
10.1609/aaai.v35i12.17331
| 220,244 | 2 |
https://scholar.google.com/scholar?cites=17657868749634336575&as_sdt=5,44&sciodt=0,44&hl=en
| 3 |
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;taobao.com
|
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;taobao.com
| 9 |
0;0;0;0;0;0;0;0;0
|
Alibaba Group
|
https://www.alibaba.com
|
Alibaba
|
0;0;0;0;0;0;0;0;0
|
Hangzhou
|
0;0;0;0;0;0;0;0;0
|
China
|
|||||
08074
|
A Recipe for Global Convergence Guarantee in Deep Neural Networks
|
main
|
Technical
|
Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent kernel (NTK) regime. This paper proposes an algorithm, which is ensured to have global convergence guarantees in the practical regime beyond the NTK regime, under a verifiable condition called the expressivity condition. The expressivity condition is defined to be both data-dependent and architecture-dependent, which is the key property that makes our results applicable for practical settings beyond the NTK regime. On the one hand, the expressivity condition is theoretically proven to hold data-independently for fully-connected deep neural networks with narrow hidden layers and a single wide layer. On the other hand, the expressivity condition is numerically shown to hold data-dependently for deep (convolutional) ResNet with batch normalization with various standard image datasets. We also show that the the proposed algorithm has generalization performances comparable with those of the heuristic algorithm, with the same hyper-parameters and total number of iterations. Therefore, the proposed algorithm can be viewed as a step towards providing theoretical guarantees for deep learning in the practical regime.
|
Machine Learning II
|
Kenji Kawaguchi; Qingyun Sun
|
Harvard University; Stanford University
|
https://cdn.aaai.org/ojs/16984/16984-13-20478-1-2-20210518.pdf
|
https://aaai.org/papers/08074-a-recipe-for-global-convergence-guarantee-in-deep-neural-networks/
|
10.1609/aaai.v35i9.16984
| 345,302 | 12 |
https://scholar.google.com/scholar?cites=15420000296183018465&as_sdt=5,44&sciodt=0,44&hl=en
| 5 |
fas.harvard.edu;stanford.edu
|
fas.harvard.edu;stanford.edu
| 2 |
0;1
|
Harvard University;Stanford University
|
;
|
https://www.harvard.edu;https://www.stanford.edu
|
Harvard;Stanford
|
1
|
;Stanford
|
0;0
|
United States
|
||||
03669
|
A SAT-based Resolution of Lam’s Problem
|
main
|
Technical
|
In 1989, computer searches by Lam, Thiel, and Swiercz experimentally resolved Lam's problem from projective geometry—the long-standing problem of determining if a projective plane of order ten exists. Both the original search and an independent verification in 2011 discovered no such projective plane. However, these searches were each performed using highly specialized custom-written code and did not produce nonexistence certificates. In this paper, we resolve Lam's problem by translating the problem into Boolean logic and use satisfiability (SAT) solvers to produce nonexistence certificates that can be verified by a third party. Our work uncovered consistency issues in both previous searches—highlighting the difficulty of relying on special-purpose search code for nonexistence results.
|
Constraint Satisfaction and Optimization
|
Curtis Bright; Kevin K. H. Cheung; Brett Stevens; Ilias Kotsireas; Vijay Ganesh
|
University of Windsor; Carleton University; Carleton University; Wilfrid Laurier University; University of Waterloo
|
https://cdn.aaai.org/ojs/16483/16483-13-19977-1-2-20210518.pdf
|
https://aaai.org/papers/03669-a-sat-based-resolution-of-lam-s-problem/
|
10.1609/aaai.v35i5.16483
| 132,406 | 30 |
https://scholar.google.com/scholar?cites=9909547568298949287&as_sdt=2005&sciodt=0,5&hl=en
| 10 |
;;;;
|
;;;;
| 5 |
0;1;1;2;3
|
University of Windsor;Carleton University;Wilfrid Laurier University;University of Waterloo
|
;;;
|
https://www.uwindsor.ca;https://carleton.ca;https://www.wlu.ca;https://uwaterloo.ca
|
UWindsor;Carleton;WLU;UW
|
0;0;0;0;0
|
Canada
|
||||||
08030
|
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints
|
main
|
Technical
|
Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages the linear programming formulation of repeated optimistic planning for finite-horizon CMDP to provide a probably approximately correctness (PAC) guarantee on the number of episodes needed to ensure a near optimal policy, i.e., with resulting objective value close to that of the optimal value and satisfying the constraints within low tolerance, with high probability. The number of episodes needed is shown to have linear dependence on the sizes of the state and action spaces and quadratic dependence on the time horizon and an upper bound on the number of possible successor states for a state-action pair. Therefore, if the upper bound on the number of possible successor states is much smaller than the size of the state space, the number of needed episodes becomes linear in the sizes of the state and action spaces and quadratic in the time horizon.
|
Machine Learning II
|
Krishna C. Kalagarla; Rahul Jain; Pierluigi Nuzzo
|
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles
|
https://cdn.aaai.org/ojs/16979/16979-13-20473-1-2-20210518.pdf
|
https://aaai.org/papers/08030-a-sample-efficient-algorithm-for-episodic-finite-horizon-mdp-with-constraints/
|
10.1609/aaai.v35i9.16979
| 164,741 | 60 |
https://scholar.google.com/scholar?cites=2106942603421949007&as_sdt=5,33&sciodt=0,33&hl=en
| 6 |
usc.edu;usc.edu;usc.edu
|
usc.edu;usc.edu;usc.edu
| 3 |
0;0;0
|
University of Southern California
|
Ming Hsieh Department of Electrical and Computer Engineering
|
https://www.usc.edu
|
USC
|
0;0;0
|
Los Angeles
|
0;0;0
|
United States
|
||||
04996
|
A Scalable Reasoning and Learning Approach for Neural-Symbolic Stream Fusion
|
AAAI Technical Track Focus Area
|
Technical
|
Driven by deep neural networks (DNN), the recent development of computer vision makes vision sensors such as stereo cameras and Lidars ubiquitous in autonomous cars, robotics and traffic monitoring. However, a traditional DNN-based data fusion pipeline like object tracking has to hard-wire an engineered set of DNN models to a fixed processing logic, which makes it difficult to infuse new models to that pipeline. To overcome this, we propose a novel neural-symbolic stream reasoning approach realised by semantic stream reasoning programs which specify DNN-based data fusion pipelines via logic rules with learnable probabilistic degrees as weights. The reasoning task over this program is governed by a novel incremental reasoning algorithm, which lends itself also as a core building block for a scalable and parallel algorithm to learn the weights for such program. Extensive experiments with our first prototype on multi-object tracking benchmarks for autonomous driving and traffic monitoring show that our flexible approach can considerably improve both accuracy and processing throughput compared to the DNN-based counterparts.
|
Neuro-Symbolic AI
|
Danh Le-Phuoc; Thomas Eiter; Anh Le-Tuan
|
Open Distributed Systems, Technical University Berlin, Germany; Institute of Logic and Computation, Vienna University of Technology (TU Wien), Austria; Open Distributed Systems, Technical University Berlin, Germany
|
https://cdn.aaai.org/ojs/16633/16633-13-20127-1-2-20210518.pdf
|
https://aaai.org/papers/04996-a-scalable-reasoning-and-learning-approach-for-neural-symbolic-stream-fusion/
|
10.1609/aaai.v35i6.16633
| 2,179,337 | 27 |
https://scholar.google.com/scholar?cites=14323685941946143947&as_sdt=5,33&sciodt=0,33&hl=en
| 4 |
tu-berlin.de;kr.tuwien.ac.at;tu-berlin.de
|
tu-berlin.de;kr.tuwien.ac.at;tu-berlin.de
| 3 |
0;1;0
|
Technical University Berlin;Vienna University of Technology
|
Open Distributed Systems;Institute of Logic and Computation
|
https://www.tu-berlin.de;https://www.tuwien.ac.at
|
TU Berlin;TU Wien
|
0;1;0
|
Berlin;Vienna
|
0;1;0
|
Germany;Austria
|
||||
03806
|
A Scalable Two Stage Approach to Computing Optimal Decision Sets
|
main
|
Technical
|
Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning behind its decisions, warranting the need for explainable AI. Rule-based models, such as decision trees, decision lists, and decision sets, are conventionally deemed to be the most interpretable. Recent work uses propositional satisfiability (SAT) solving (and its optimization variants) to generate minimum-size decision sets. Motivated by limited practical scalability of these earlier methods, this paper proposes a novel approach to learn minimum-size decision sets by enumerating individual rules of the target decision set independently of each other, and then solving a set cover problem to select a subset of rules. The approach makes use of modern maximum satisfiability and integer linear programming technologies. Experiments on a wide range of publicly available datasets demonstrate the advantage of the new approach over the state of the art in SAT-based decision set learning.
|
Constraint Satisfaction and Optimization
|
Alexey Ignatiev; Edward Lam; Peter J. Stuckey; Joao Marques-Silva
|
Monash University, Melbourne, Australia; Monash University, Melbourne, Australia + CSIRO Data61, Melbourne, Australia; Monash University, Melbourne, Australia; ANITI, IRIT, CNRS, Toulouse, France
|
https://cdn.aaai.org/ojs/16498/16498-13-19992-1-2-20210518.pdf
|
https://aaai.org/papers/03806-a-scalable-two-stage-approach-to-computing-optimal-decision-sets/
|
10.1609/aaai.v35i5.16498
| 264,824 | 19 |
https://scholar.google.com/scholar?cites=15936899524800868669&as_sdt=2005&sciodt=0,5&hl=en
| 12 |
monash.edu;monash.edu;monash.edu;irit.fr
|
monash.edu;monash.edu;monash.edu;irit.fr
| 4 |
0;0+1;0;2
|
Monash University;CSIRO Data61;ANITI
|
;;IRIT
|
https://www.monash.edu;https://www.csiro.au/en/Research/Data61;
|
Monash;CSIRO Data61;
|
0;0+0;0
|
Melbourne;
|
0;0+0;0;1
|
Australia;France
|
||||
03697
|
A Sharp Leap from Quantified Boolean Formula to Stochastic Boolean Satisfiability Solving
|
main
|
Technical
|
Stochastic Boolean Satisfiability (SSAT) is a powerful representation for the concise encoding of quantified decision problems with uncertainty. While it shares commonalities with quantified Boolean formula (QBF) satisfiability and has the same PSPACE-complete complexity, SSAT solving tends to be more challenging as it involves expensive model counting, a.k.a. Sharp-SAT. To date, SSAT solvers, especially those imposing no restrictions on quantification levels, remain much lacking. In this paper, we present a new SSAT solver based on the framework of clause selection and cube distribution previously proposed for QBF solving. With model counting integrated and learning techniques strengthened, our solver is general and effective. Experimental results demonstrate the overall superiority of the proposed algorithm in both solving performance and memory usage compared to the state-of-the-art solvers on a number of benchmark formulas.
|
Constraint Satisfaction and Optimization
|
Pei-Wei Chen; Yu-Ching Huang; Jie-Hong R. Jiang
|
Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan + Graduate Institute of Electronics Engineering, National Taiwan University, Taipei 10617, Taiwan
|
https://cdn.aaai.org/ojs/16486/16486-13-19980-1-2-20210518.pdf
|
https://aaai.org/papers/03697-a-sharp-leap-from-quantified-boolean-formula-to-stochastic-boolean-satisfiability-solving/
|
10.1609/aaai.v35i5.16486
| 182,729 | 19 |
https://scholar.google.com/scholar?cites=13557140933685226222&as_sdt=80000005&sciodt=0,23&hl=en
| 3 |
gmail.com;gmail.com;ntu.edu.tw
|
gmail.com;gmail.com;ntu.edu.tw
| 3 |
0;0;0+0
|
National Taiwan University
|
Department of Electrical Engineering
|
https://www.ntu.edu.tw
|
NTU
|
0;0;0+0
|
Taipei
|
0;0;0+0
|
Taiwan, China
|
||||
06331
|
A Simple Framework for Cognitive Planning
|
main
|
Technical
|
We present a novel approach to cognitive planning, i.e., an agent's planning aimed at changing the cognitive attitudes of another agent including her beliefs and intentions. We encode the cognitive planning problem in an epistemic logic with a semantics exploiting belief bases. We study a NP-fragment of the logic whose satisfiability problem is reduced to SAT. We provide complexity results for the cognitive planning problem. Moreover, we illustrate its potential for applications in human-machine interaction in which an artificial agent is expected to interact with a human agent through dialogue and to persuade the human to behave in a certain way.
|
Knowledge Representation and Reasoning
|
Jorge Luis Fernandez Davila; Dominique Longin; Emiliano Lorini; Frédéric Maris
|
IRIT, Toulouse University, France; IRIT, CNRS, Toulouse University, France; IRIT, CNRS, Toulouse University, France; IRIT, Toulouse University, France
|
https://cdn.aaai.org/ojs/16786/16786-13-20280-1-2-20210518.pdf
|
https://aaai.org/papers/06331-a-simple-framework-for-cognitive-planning/
|
10.1609/aaai.v35i7.16786
| 185,122 | 11 |
https://scholar.google.com/scholar?cites=11849657532595832396&as_sdt=2005&sciodt=0,5&hl=en
| 15 |
irit.fr;irit.fr;irit.fr;irit.fr
|
irit.fr;irit.fr;irit.fr;irit.fr
| 4 |
0;0;0;0
|
Toulouse University
|
Institut de Recherche en Informatique de Toulouse (IRIT)
|
https://www.univ-toulouse.fr
|
UT
|
0;0
|
Toulouse;
|
0;0;0;0
|
France
|
||||
13815
|
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
|
main
|
Technical
|
Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning based topic models, specifically aspect-based autoencoder, suffer from several problems such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest. To tackle these challenges, in this paper, we first propose a self-supervised contrastive learning framework and an attention-based model equipped with a novel smooth self-attention (SSA) module for the UAD task in order to learn better representations for aspects and review segments. Secondly, we introduce a high-resolution selective mapping (HRSMap) method to efficiently assign aspects discovered by the model to the aspects of interest. We also propose using a knowledge distillation technique to further improve the aspect detection performance. Our methods outperform several recent unsupervised and weakly supervised approaches on publicly available benchmark user review datasets. Aspect interpretation results show that extracted aspects are meaningful, have a good coverage, and can be easily mapped to aspects of interest. Ablation studies and attention weight visualization also demonstrate effectiveness of SSA and the knowledge distillation method.
|
Speech and Natural Language Processing II
|
Tian Shi; Liuqing Li; Ping Wang; Chandan K. Reddy
|
Department of Computer Science, Virginia Tech; Verizon Media; Department of Computer Science, Virginia Tech; Department of Computer Science, Virginia Tech
|
https://cdn.aaai.org/ojs/17628/17628-13-21122-1-2-20210518.pdf
|
https://aaai.org/papers/13815-a-simple-and-effective-self-supervised-contrastive-learning-framework-for-aspect-detection/
|
10.1609/aaai.v35i15.17628
| 336,636 | 51 |
https://scholar.google.com/scholar?cites=17446819308138120373&as_sdt=2005&sciodt=0,5&hl=en
| 9 |
vt.edu;verizonmedia.com;vt.edu;cs.vt.edu
|
vt.edu;verizonmedia.com;vt.edu;cs.vt.edu
| 4 |
0;1;0;0
|
Virginia Tech;Verizon Media
|
Department of Computer Science;
|
https://www.vt.edu;https://www.verizonmedia.com
|
VT;VM
|
0;0;0;0
|
United States
|
||||||
00733
|
A Spatial Regulated Patch-Wise Approach for Cervical Dysplasia Diagnosis
|
main
|
Technical
|
Cervical dysplasia diagnosis via visual investigation is a challenging problem. Recent approaches use deep learning techniques to extract features and require the downsampling of high-resolution cervical screening images to smaller sizes for training. Such a reduction may result in the loss of visual details that appear weakly and locally within a cervical image. To overcome this challenge, our work divides an image into patches and then represents it from patch features. We aggregate patch patterns into an image feature in a weighted manner by considering the patch--image label relation. The weights are visualized as a heatmap to explain where the diagnosis results come from. We further introduce a spatial regulator to guide the classifier to focus on the cervix region and to adjust the weight distribution, without requiring any manual annotations of the cervix region. A novel iterative algorithm is designed to refine the regulator, which is able to capture the variations in cervix center locations and shapes. Experiments on an 18-year real-world dataset indicate a minimal of 3.47%, 4.59%, 8.54% improvements over the state-of-the-art in accuracy, F1, and recall measures, respectively.
|
Application Domains
|
Ying Zhang; Yifang Yin; Zhenguang Liu; Roger Zimmermann
|
School of Computing, National University of Singapore + School of Computer Science, Northwestern Polytechnical University; School of Computing, National University of Singapore; School of Computer and Information Engineering, Zhejiang Gongshang University; School of Computing, National University of Singapore
|
https://cdn.aaai.org/ojs/16154/16154-13-19648-1-2-20210518.pdf
|
https://aaai.org/papers/00733-a-spatial-regulated-patch-wise-approach-for-cervical-dysplasia-diagnosis/
|
10.1609/aaai.v35i1.16154
| 2,010,290 | 8 |
https://scholar.google.com/scholar?cites=3294140934780967106&as_sdt=5,48&sciodt=0,48&hl=en
| 3 |
nus.edu.sg;nus.edu.sg;nus.edu.sg;gmail.com
|
nus.edu.sg;nus.edu.sg;nus.edu.sg;gmail.com
| 4 |
0+1;0;2;0
|
National University of Singapore;Northwestern Polytechnical University;Zhejiang Gongshang University
|
School of Computing;School of Computer Science;School of Computer and Information Engineering
|
https://www.nus.edu.sg;https://www.nwpu.edu.cn;http://www.hgh.edu.cn
|
NUS;NWPU;
|
0+1;0;1;0
|
Singapore;China
|
||||||
13692
|
A Student-Teacher Architecture for Dialog Domain Adaptation Under the Meta-Learning Setting
|
main
|
Technical
|
Numerous new dialog domains are being created every day while collecting data for these domains is extremely costly since it involves human interactions. Therefore, it is essential to develop algorithms that can adapt to different domains efficiently when building data-driven dialog models. Most recent research on domain adaption focuses on giving the model a better initialization, rather than optimizing the adaptation process. We propose an efficient domain adaptive task-oriented dialog system model, which incorporates a meta-teacher model to emphasize the different impacts between generated tokens with respect to the context. We first train our base dialog model and meta-teacher model adversarially in a meta-learning setting on rich-resource domains. The meta-teacher learns to quantify the importance of tokens under different contexts across different domains. During adaptation, the meta-teacher guides the dialog model to focus on important tokens in order to achieve better adaptation efficiency. We evaluate our model on two multi-domain datasets, MultiWOZ and Google Schema-Guided Dialogue, and achieve state-of-the-art performance.
|
Speech and Natural Language Processing II
|
Kun Qian; Wei Wei; Zhou Yu
|
University of California, Davis; Google Inc.; University of California, Davis
|
https://cdn.aaai.org/ojs/17614/17614-13-21108-1-2-20210518.pdf
|
https://aaai.org/papers/13692-a-student-teacher-architecture-for-dialog-domain-adaptation-under-the-meta-learning-setting/
|
10.1609/aaai.v35i15.17614
| 467,845 | 7 |
https://scholar.google.com/scholar?cites=17221603733259094200&as_sdt=5,33&sciodt=0,33&hl=en
| 4 |
ucdavis.edu;google.com;ucdavis.edu
|
ucdavis.edu;google.com;ucdavis.edu
| 3 |
0;1;0
|
University of California, Davis;Google
|
;
|
https://www.ucdavis.edu;https://www.google.com
|
UC Davis;Google
|
0;1;0
|
Davis;Mountain View
|
0;0;0
|
United States
|
||||
14185
|
A Supervised Multi-Head Self-Attention Network for Nested Named Entity Recognition
|
main
|
Technical
|
In recent years, researchers have shown an increased interest in recognizing the overlapping entities that have nested structures. However, most existing models ignore the semantic correlation between words under different entity types. Considering words in sentence play different roles under different entity types, we argue that the correlation intensities of pairwise words in sentence for each entity type should be considered. In this paper, we treat named entity recognition as a multi-class classification of word pairs and design a simple neural model to handle this issue. Our model applies a supervised multi-head self-attention mechanism, where each head corresponds to one entity type, to construct the word-level correlations for each type. Our model can flexibly predict the span type by the correlation intensities of its head and tail under the corresponding type. In addition, we fuse entity boundary detection and entity classification by a multitask learning framework, which can capture the dependencies between these two tasks. To verify the performance of our model, we conduct extensive experiments on both nested and flat datasets. The experimental results show that our model can outperform the previous state-of-the-art methods on multiple tasks without any extra NLP tools or human annotations.
|
Speech and Natural Language Processing III
|
Yongxiu Xu; Heyan Huang; Chong Feng; Yue Hu
|
Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China; School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China; School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China+School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
|
https://cdn.aaai.org/ojs/17669/17669-13-21163-1-2-20210518.pdf
|
https://aaai.org/papers/14185-a-supervised-multi-head-self-attention-network-for-nested-named-entity-recognition/
|
10.1609/aaai.v35i16.17669
| 324,966 | 48 |
https://scholar.google.com/scholar?cites=16750389308284504162&as_sdt=5,44&sciodt=0,44&hl=en
| 5 |
iie.ac.cn;bit.edu.cn;bit.edu.cn;iie.ac.cn
|
iie.ac.cn;bit.edu.cn;bit.edu.cn;iie.ac.cn
| 4 |
0+1;2;2;0+1
|
Chinese Academy of Sciences;University of Chinese Academy of Sciences;Beijing Institute of Technology
|
Institute of Information Engineering;School of Cyber Security;School of Computer Science and Technology
|
http://www.cas.cn;http://www.ucas.ac.cn;http://www.bit.edu.cn
|
CAS;UCAS;BIT
|
0+0;0;0;0+0
|
Beijing
|
0+0;0;0;0+0
|
China
|
||||
01379
|
A Systematic Evaluation of Object Detection Networks for Scientific Plots
|
main
|
Technical
|
Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different than the objects found in natural images? To answer this question, we train and compare the accuracy of Fast/Faster R-CNN, SSD, YOLO and RetinaNet on the PlotQA dataset with over 220,000 scientific plots. At the standard IOU setting of 0.5, most networks perform well with mAP scores greater than 80% in detecting the relatively simple objects in plots. However, the performance drops drastically when evaluated at a stricter IOU of 0.9 with the best model giving a mAP of 35.70%. Note that such a stricter evaluation is essential when dealing with scientific plots where even minor localisation errors can lead to large errors in downstream numerical inferences. Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks. While this significantly improves the performance, there are still two main issues: (i) performance on text objects which are essential for reasoning is very poor, and (ii) inference time is unacceptably large considering the simplicity of plots. To solve this open problem, we make a series of contributions: (a) an efficient region proposal method based on Laplacian edge detectors, (b) a feature representation of region proposals that includes neighbouring information, (c) a linking component to join multiple region proposals for detecting longer textual objects, and (d) a custom loss function that combines a smooth L1-loss with an IOU-based loss. Combining these ideas, our final model is very accurate at extreme IOU values achieving a mAP of 93.44%@0.9 IOU. Simultaneously, our model is very efficient with an inference time 16x lesser than the current models, including one-stage detectors. Our model also achieves a high accuracy on an extrinsic plot-to-table conversion task with an F1 score of 0.77. With these contributions, we make a definitive progress in object detection for plots and enable further exploration on automated reasoning of plots.
|
Computer Vision I
|
Pritha Ganguly; Nitesh S Methani; Mitesh M. Khapra; Pratyush Kumar
|
Robert Bosch Centre for Data Science and AI (RBC-DSAI), Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India; Robert Bosch Centre for Data Science and AI (RBC-DSAI), Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India; Robert Bosch Centre for Data Science and AI (RBC-DSAI), Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India; Robert Bosch Centre for Data Science and AI (RBC-DSAI), Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India
|
https://cdn.aaai.org/ojs/16227/16227-13-19721-1-2-20210518.pdf
|
https://aaai.org/papers/01379-a-systematic-evaluation-of-object-detection-networks-for-scientific-plots/
|
10.1609/aaai.v35i2.16227
| 1,728,658 | 9 |
https://scholar.google.com/scholar?cites=14585704429518759854&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
cse.iitm.ac.in;cse.iitm.ac.in;cse.iitm.ac.in;cse.iitm.ac.in
|
cse.iitm.ac.in;cse.iitm.ac.in;cse.iitm.ac.in;cse.iitm.ac.in
| 4 |
0;0;0;0
|
Indian Institute of Technology Madras
|
Department of Computer Science and Engineering
|
https://www.iitm.ac.in
|
IIT Madras
|
0;0;0;0
|
Chennai
|
0;0;0;0
|
India
|
||||
12848
|
A Theoretical Analysis of the Repetition Problem in Text Generation
|
main
|
Technical
|
Text generation tasks, including translation, summarization, language models, and etc. see rapid growth during recent years. Despite the remarkable achievements, the repetition problem has been observed in nearly all text generation models undermining the generation performance extensively. To solve the repetition problem, many methods have been proposed, but there is no existing theoretical analysis to show why this problem happens and how it is resolved. In this paper, we propose a new framework for theoretical analysis for the repetition problem. We first define the Average Repetition Probability (ARP) to characterize the repetition problem quantitatively. Then, we conduct an extensive analysis of the Markov generation model and derive several upper bounds of the average repetition probability with intuitive understanding. We show that most of the existing methods are essentially minimizing the upper bounds explicitly or implicitly. Grounded on our theory, we show that the repetition problem is, unfortunately, caused by the traits of our language itself. One major reason is attributed to the fact that there exist too many words predicting the same word as the subsequent word with high probability. Consequently, it is easy to go back to that word and form repetitions and we dub it as the high inflow problem. Furthermore, we extend our analysis to broader generation models by deriving a concentration bound of the average repetition probability for a general generation model. Finally, based on the theoretical upper bounds, we propose a novel rebalanced encoding approach to alleviate the high inflow problem and thus reducing the upper bound. The experimental results show that our theoretical framework is applicable in general generation models and our proposed rebalanced encoding approach alleviates the repetition problem significantly in both the translation task and the language modeling task. The source code of this paper can be obtained from https://github.com/fuzihaofzh/repetition-problem-nlg.
|
Speech and Natural Language Processing I
|
Zihao Fu; Wai Lam; Anthony Man-Cho So; Bei Shi
|
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong; Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong; Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong; AI Lab, Tencent
|
https://cdn.aaai.org/ojs/17520/17520-13-21014-1-2-20210518.pdf
|
https://aaai.org/papers/12848-a-theoretical-analysis-of-the-repetition-problem-in-text-generation/
|
10.1609/aaai.v35i14.17520
| 193,469 | 105 |
https://scholar.google.com/scholar?cites=7535305321646012771&as_sdt=80000005&sciodt=0,23&hl=en
| 7 |
se.cuhk.edu.hk;se.cuhk.edu.hk;se.cuhk.edu.hk;tencent.com
|
se.cuhk.edu.hk;se.cuhk.edu.hk;se.cuhk.edu.hk;tencent.com
|
https://github.com/fuzihaofzh/repetition-problem-nlg
| 4 |
0;0;0;1
|
The Chinese University of Hong Kong;Tencent
|
Department of Systems Engineering and Engineering Management;AI Lab
|
https://www.cuhk.edu.hk;https://www.tencent.com
|
CUHK;Tencent
|
0;0;0
|
Hong Kong;
|
0;0;0;0
|
China
|
|||
06741
|
A Theory of Independent Mechanisms for Extrapolation in Generative Models
|
main
|
Technical
|
Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. We demonstrate on toy examples that classical stochastic gradient descent can hinder the model's extrapolation capabilities, suggesting independence of mechanisms should be enforced explicitly during training. Experiments on deep generative models trained on real world data support these insights and illustrate how the extrapolation capabilities of such models can be leveraged.
|
Machine Learning I
|
Michel Besserve; Remy Sun; Dominik Janzing; Bernhard Schölkopf
|
Max Planck Institute for Intelligent Systems, Tubingen, Germany+Max Planck Institute for Biological Cybernetics, Tubingen, Germany; Max Planck Institute for Intelligent Systems, Tubingen, Germany+ENS Rennes, France; Max Planck Institute for Intelligent Systems, Tubingen, Germany; Max Planck Institute for Intelligent Systems, Tubingen, Germany
|
https://cdn.aaai.org/ojs/16833/16833-13-20327-1-2-20210518.pdf
|
https://aaai.org/papers/06741-a-theory-of-independent-mechanisms-for-extrapolation-in-generative-models/
|
10.1609/aaai.v35i8.16833
| 2,381,355 | 25 |
https://scholar.google.com/scholar?cites=12544857898599554908&as_sdt=2005&sciodt=0,5&hl=en
| 7 |
tuebingen.mpg.de;ens-rennes.fr;amazon.de;tuebingen.mpg.de
|
tuebingen.mpg.de;ens-rennes.fr;amazon.de;tuebingen.mpg.de
|
https://arxiv.org/abs/2004.00184
| 4 |
0+1;0+2;0;0
|
Max Planck Institute for Intelligent Systems;Max Planck Institute for Biological Cybernetics;École Normale Supérieure de Rennes
|
;;
|
https://www.mpi-is.mpg.de;https://www.biocybernetics.mpg.de;https://www.ens-rennes.fr
|
MPI-IS;MPIBC;ENS Rennes
|
0+0;0+1;0;0
|
Tubingen;Rennes
|
0+0;0+1;0;0
|
Germany;France
|
|||
07519
|
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient
|
main
|
Technical
|
Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factored approximations such as Kronecker-factored Approximate Curvature (KFAC), we propose a new approximation to the Fisher information matrix (FIM) called Trace-restricted Kronecker-factored Approximate Curvature (TKFAC), which can hold the certain trace relationship between the exact and the approximate FIM. In TKFAC, we decompose each block of the approximate FIM as a Kronecker product of two smaller matrices and scaled by a coefficient related to trace. We theoretically analyze TKFAC's approximation error and give an upper bound of it. We also propose a new damping technique for TKFAC on convolutional neural networks to maintain the superiority of second-order optimization methods during training. Experiments show that our method has better performance compared with several state-of-the-art algorithms on some deep network architectures.
|
Machine Learning II
|
Kaixin Gao; Xiaolei Liu; Zhenghai Huang; Min Wang; Zidong Wang; Dachuan Xu; Fan Yu
|
School of Mathematics, Tianjin University, China; School of Mathematics, Tianjin University, China; School of Mathematics, Tianjin University, China; Central Software Institute, Huawei Technologies Co. Ltd, China; Central Software Institute, Huawei Technologies Co. Ltd, China; Department of Operations Research and Information Engineering, Beijing University of Technology, China; Central Software Institute, Huawei Technologies Co. Ltd, China
|
https://cdn.aaai.org/ojs/16921/16921-13-20415-1-2-20210518.pdf
|
https://aaai.org/papers/07519-a-trace-restricted-kronecker-factored-approximation-to-natural-gradient/
|
10.1609/aaai.v35i9.16921
| 2,100,531 | 13 |
https://scholar.google.com/scholar?cites=17693946134822172435&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
tju.edu.cn;tju.edu.cn;tju.edu.cn;huawei.com;huawei.com;bjut.edu.cn;huawei.com
|
tju.edu.cn;tju.edu.cn;tju.edu.cn;huawei.com;huawei.com;bjut.edu.cn;huawei.com
| 7 |
0;0;0;1;1;2;1
|
Tianjin University;Huawei Technologies Co. Ltd;Beijing University of Technology
|
School of Mathematics;Central Software Institute;Department of Operations Research and Information Engineering
|
http://www.tju.edu.cn;https://www.huawei.com;http://www.bjut.edu.cn
|
Tianjin University;Huawei;BJUT
|
0;0;0;0;0;0;0
|
China
|
||||||
05016
|
A Unified Framework for Planning with Learned Neural Network Transition Models
|
AAAI Technical Track Focus Area
|
Technical
|
Automated planning with neural network transition models is a two stage approach to solving planning problems with unknown transition models. The first stage of the approach learns the unknown transition model from data as a neural network model, and the second stage of the approach compiles the learned model to either a Mixed-Integer Linear Programming (MILP) model or a Recurrent Neural Network (RNN) model, and optimize it using an off-the-shelf solver. The previous studies have shown that both models have their advantages and disadvantages. Namely, the MILP model can be solved optimally using a branch-and-bound algorithm but has been experimentally shown not to scale well for neural networks with multiple hidden layers. In contrast, the RNN model can be solved effectively using a gradient descent algorithm but can only work under very restrictive assumptions. In this paper, we focus on improving the effectiveness of solving the second stage of the approach by introducing (i) a novel Lagrangian RNN architecture that can model the previously ignored components of the planning problem as Lagrangian functions, and (ii) a novel framework that unifies the MILP and the Lagrangian RNN models such that the weakness of one model is complemented by the strength of the other. Experimentally, we show that our unifying framework significantly outperforms the standalone MILP model by solving 80% more problem instances, and showcase the ability of our unifying framework to find high quality solutions to challenging automated planning problems with unknown transition models.
|
Neuro-Symbolic AI
|
Buser Say
|
Monash University, Melbourne, Victoria, Australia
|
https://cdn.aaai.org/ojs/16635/16635-13-20129-1-2-20210518.pdf
|
https://aaai.org/papers/05016-a-unified-framework-for-planning-with-learned-neural-network-transition-models/
|
10.1609/aaai.v35i6.16635
| 797,382 | 10 |
https://scholar.google.com/scholar?cites=149461290138185087&as_sdt=2005&sciodt=0,5&hl=en
| 4 |
monash.edu
|
monash.edu
| 1 |
0
|
Monash University
|
https://www.monash.edu
|
Monash
|
0
|
Melbourne
|
0
|
Australia
|
|||||
01097
|
A Unified Multi-Scenario Attacking Network for Visual Object Tracking
|
main
|
Technical
|
Existing methods of adversarial attacks successfully generate adversarial examples to confuse Deep Neural Networks (DNNs) of image classification and object detection, resulting in wrong predictions. However, these methods are difficult to attack models of video object tracking, because the tracking algorithms could handle sequential information across video frames and the categories of targets tracked are normally unknown in advance. In this paper, we propose a Unified and Effective Network, named UEN, to attack visual object tracking models. There are several appealing characteristics of UEN: (1) UEN could produce various invisible adversarial perturbations according to different attack settings by using only one simple end-to-end network with three ingenious loss function; (2) UEN could generate general visible adversarial patch patterns to attack the advanced trackers in the real-world; (3) Extensive experiments show that UEN is able to attack many state-of-the-art trackers effectively (e.g. SiamRPN-based networks and DiMP) on popular tracking datasets including OTB100, UAV123, and GOT10K, making online real-time attacks possible. The attack results outperform the introduced baseline in terms of attacking ability and attacking efficiency.
|
Computer Vision I
|
Xuesong Chen; Canmiao Fu; Feng Zheng; Yong Zhao; Hongsheng Li; Ping Luo; Guo-Jun Qi
|
The Chinese University of Hong Kong; WeChat AI, Tencent; Peking University + Depatment of Computer Science and Engineering, Southern University of Science and Technology; Peking University; The Chinese University of Hong Kong; The University of Hong Kong; Laboratory for MAPLE, Futurewei Technologies
|
https://cdn.aaai.org/ojs/16195/16195-13-19689-1-2-20210518.pdf
|
https://aaai.org/papers/01097-a-unified-multi-scenario-attacking-network-for-visual-object-tracking/
|
10.1609/aaai.v35i2.16195
| 3,216,057 | 19 |
https://scholar.google.com/scholar?cites=9911979789627405146&as_sdt=5,48&sciodt=0,48&hl=en
| 4 |
;;pku.edu.cn;sustech.edu.cn;;;
|
;;pku.edu.cn;sustech.edu.cn;;;
| 7 |
0;1;2+3;2;0;4;5
|
The Chinese University of Hong Kong;Tencent;Peking University;Southern University of Science and Technology;The University of Hong Kong;Futurewei Technologies
|
;WeChat AI;;Department of Computer Science and Engineering;;Laboratory for MAPLE
|
https://www.cuhk.edu.hk;https://www.tencent.com;http://www.pku.edu.cn;https://www.sustech.edu.cn;https://www.hku.hk;https://www.futurewei.com
|
CUHK;Tencent;Peking U;SUSTech;HKU;Futurewei
|
0;0;0+0;0;0;0;1
|
China;United States
|
||||||
14524
|
A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations
|
main
|
Technical
|
Joint extraction of entities and relations focuses on detecting entity pairs and their relations simultaneously with a unified model. Based on the extraction order, previous works mainly solve this task through relation-last, relation-first and relation-middle manner. However, these methods still suffer from the template-dependency, non-entity detection and non-predefined relation prediction problem. To overcome these challenges, in this paper, we propose a unified multi-task learning framework to divide the task into three interacted sub-tasks. Specifically, we first introduce the type-attentional method for subject extraction to provide prior type information explicitly. Then, the subject-aware relation prediction is presented to select useful relations based on the combination of global and local semantics. Third, we propose a question generation based QA method for object extraction to obtain diverse queries automatically. Notably, our method detects subjects or objects without relying on NER models and thus it is capable of dealing with the non-entity scenario. Finally, three sub-tasks are integrated into a unified model through parameter sharing. Extensive experiments demonstrate that the proposed framework outperforms all the baseline methods on two benchmark datasets, and further achieve excellent performance for non-predefined relations.
|
Speech and Natural Language Processing III
|
Tianyang Zhao; Zhao Yan; Yunbo Cao; Zhoujun Li
|
State Key Lab of Software Development Environment, Beihang University, Beijing, China; Tencent Cloud Xiaowei, Beijing, China; Tencent Cloud Xiaowei, Beijing, China; State Key Lab of Software Development Environment, Beihang University, Beijing, China
|
https://cdn.aaai.org/ojs/17707/17707-13-21201-1-2-20210518.pdf
|
https://aaai.org/papers/14524-a-unified-multi-task-learning-framework-for-joint-extraction-of-entities-and-relations/
|
10.1609/aaai.v35i16.17707
| 901,620 | 21 |
https://scholar.google.com/scholar?cites=11287306557722548168&as_sdt=2005&sciodt=0,5&hl=en
| 3 |
buaa.edu.cn;tencent.com;tencent.com;buaa.edu.cn
|
buaa.edu.cn;tencent.com;tencent.com;buaa.edu.cn
| 4 |
0;1;1;0
|
Beihang University;Tencent Cloud Xiaowei
|
State Key Lab of Software Development Environment;
|
http://www.buaa.edu.cn;https://cloud.tencent.com
|
BUAA;Tencent Cloud
|
0;0;0;0
|
Beijing
|
0;0;0;0
|
China
|
||||
04555
|
A Unified Pretraining Framework for Passage Ranking and Expansion
|
main
|
Technical
|
Pretrained language models have recently advanced a wide range of natural language processing tasks. Nowadays, the application of pretrained language models to IR tasks has also achieved impressive results. Typical methods either directly apply a pretrained model to improve the re-ranking stage, or use it to conduct passage expansion and term weighting for first-stage retrieval. We observe that the passage ranking and passage expansion tasks share certain inherent relations, and can benefit from each other. Therefore, in this paper, we propose a general pretraining framework to enhance both tasks with Unified Encoder-Decoder networks (UED). The overall ranking framework consists of two parts in a cascade manner: (1) passage expansion with a pretraining-based query generation method; (2) re-ranking of passage candidates from a traditional retrieval method with a pretrained transformer encoder. Both the two parts are based on the same pretrained UED model, where we jointly train the passage ranking and query generation tasks for further improving the full ranking pipeline. An extensive set of experiments have been conducted on two large-scale passage retrieval datasets to demonstrate the state-of-the-art results of the proposed framework in both the first-stage retrieval and the final re-ranking. In addition, we successfully deploy the framework to our online production system, which can stably serve industrial applications with a request volume of up to 100 QPS in less than 300ms.
|
Data Mining and Knowledge Management
|
Ming Yan; Chenliang Li; Bin Bi; Wei Wang; Songfang Huang
|
Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group; Alibaba Group
|
https://cdn.aaai.org/ojs/16584/16584-13-20078-1-2-20210518.pdf
|
https://aaai.org/papers/04555-a-unified-pretraining-framework-for-passage-ranking-and-expansion/
|
10.1609/aaai.v35i5.16584
| 496,331 | 21 |
https://scholar.google.com/scholar?cites=17264926351877852325&as_sdt=5,44&sciodt=0,44&hl=en
| 3 |
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com
|
alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com;alibaba-inc.com
| 5 |
0;0;0;0;0
|
Alibaba Group
|
https://www.alibaba.com
|
Alibaba
|
0;0;0;0;0
|
China
|
|||||||
11462
|
A Unified Taylor Framework for Revisiting Attribution Methods
|
main
|
Technical
|
Attribution methods have been developed to understand the decision making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features. Existing attribution methods often built upon empirical intuitions and heuristics. There still lacks a general and theoretical framework that not only can unify these attribution methods, but also theoretically reveal their rationales, fidelity, and limitations. To bridge the gap, in this paper, we propose a Taylor attribution framework and reformulate seven mainstream attribution methods into the framework. Based on reformulations, we analyze the attribution methods in terms of rationale, fidelity, and limitation. Moreover, We establish three principles for a good attribution in the Taylor attribution framework, i.e., low approximation error, correct contribution assignment, and unbiased baseline selection. Finally, we empirically validate the Taylor reformulations, and reveal a positive correlation between the attribution performance and the number of principles followed by the attribution method via benchmarking on real-world datasets.
|
Philosophy and Ethics of AI
|
Huiqi Deng; Na Zou; Mengnan Du; Weifu Chen; Guocan Feng; Xia Hu
|
Sun Yat-Sen University; Texas A&M University; Texas A&M University; Sun Yat-Sen University; Sun Yat-Sen University; Texas A&M University
|
https://cdn.aaai.org/ojs/17365/17365-13-20859-1-2-20210518.pdf
|
https://aaai.org/papers/11462-a-unified-taylor-framework-for-revisiting-attribution-methods/
|
10.1609/aaai.v35i13.17365
| 971,011 | 20 |
https://scholar.google.com/scholar?cites=18411561996672591088&as_sdt=5,48&sciodt=0,48&hl=en
| 6 |
mail2.sysu.edu.cn;tamu.edu;tamu.edu;mail.sysu.edu.cn;mail.sysu.edu.cn;tamu.edu
|
mail2.sysu.edu.cn;tamu.edu;tamu.edu;mail.sysu.edu.cn;mail.sysu.edu.cn;tamu.edu
| 6 |
0;1;1;0;0;1
|
Sun Yat-Sen University;Texas A&M University
|
;
|
http://www.sysu.edu.cn/;https://www.tamu.edu
|
SYSU;TAMU
|
0;1;1;0;0;1
|
China;United States
|
||||||
03984
|
A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models
|
main
|
Technical
|
Sequential recommender systems (SRS) have become a research hotspot in recent studies. Because of the requirement in capturing user's dynamic interests, sequential neural network based recommender models often need to be stacked with more hidden layers (e.g., up to 100 layers) compared with standard collaborative filtering methods. However, the high network latency has become the main obstacle when deploying very deep recommender models into a production environment. In this paper, we argue that the typical prediction framework that treats all users equally during the inference phase is inefficient in running time, as well as sub-optimal in accuracy. To resolve such an issue, we present SkipRec, an adaptive inference framework by learning to skip inactive hidden layers on a per-user basis. Specifically, we devise a policy network to automatically determine which layers should be retained and which layers are allowed to be skipped, so as to achieve user-specific decisions. To derive the optimal skipping policy, we propose using gumbel softmax and reinforcement learning to solve the non-differentiable problem during backpropagation. We perform extensive experiments on three real-world recommendation datasets, and demonstrate that SkipRec attains comparable or better accuracy with much less inference time.
|
Data Mining and Knowledge Management
|
Lei Chen; Fajie Yuan; Jiaxi Yang; Xiang Ao; Chengming Li; Min Yang
|
Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences+University of Chinese Academy of Sciences; Tencent; Huazhong University of Science and Technology; Institute of Computing Technology, Chinese Academy of Sciences; Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences; Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
|
https://cdn.aaai.org/ojs/16518/16518-13-20012-1-2-20210518.pdf
|
https://aaai.org/papers/03984-a-user-adaptive-layer-selection-framework-for-very-deep-sequential-recommender-models/
|
10.1609/aaai.v35i5.16518
| 630,657 | 12 |
https://scholar.google.com/scholar?cites=213854267943835613&as_sdt=40005&sciodt=0,10&hl=en
| 4 |
siat.ac.cn;tencent.com;hust.edu.cn;ict.ac.cn;siat.ac.cn;siat.ac.cn
|
siat.ac.cn;tencent.com;hust.edu.cn;ict.ac.cn;siat.ac.cn;siat.ac.cn
| 6 |
0+1;2;3;0;0;0
|
Chinese Academy of Sciences;University of Chinese Academy of Sciences;Tencent Holdings Limited;Huazhong University of Science and Technology
|
Shenzhen Key Laboratory for High Performance Data Mining;;;
|
http://www.siat.cas.cn;http://www.ucas.ac.cn;https://www.tencent.com;http://www.hust.edu.cn
|
CAS;UCAS;Tencent;HUST
|
0;0;0
|
Shenzhen;
|
0+0;0;0;0;0;0
|
China
|
||||
07857
|
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
|
main
|
Technical
|
Stochastic gradient descent (SGD) is a widely used method for its outstanding generalization ability and simplicity. Adaptive gradient methods have been proposed to further accelerate the optimization process. In this paper, we revisit existing adaptive gradient optimization methods with a new interpretation. Such new perspective leads to a refreshed understanding of the roles of second moments in stochastic optimization. Based on this, we propose Angle-Calibration Moment method (ACMo), a novel stochastic optimization method. It enjoys the benefits of second moments with only first moment updates. Theoretical analysis shows that ACMo is able to achieve the same convergence rate as mainstream adaptive methods. Experiments on a variety of CV and NLP tasks demonstrate that ACMo has a comparable convergence to state-of-the-art Adam-type optimizers, and even a better generalization performance in most cases. The code is available at https://github.com/Xunpeng746/ACMo.
|
Machine Learning II
|
Xunpeng Huang; Runxin Xu; Hao Zhou; Zhe Wang; Zhengyang Liu; Lei Li
|
Bytedance AI Lab, Shanghai, China; Peking University, Beijing, China; Bytedance AI Lab, Shanghai, China; Ohio State University, Columbus, Ohio, United States; Beijing Institute of Technology, Beijing, China; Bytedance AI Lab, Shanghai, China
|
https://cdn.aaai.org/ojs/16959/16959-13-20453-1-2-20210518.pdf
|
https://aaai.org/papers/07857-acmo-angle-calibrated-moment-methods-for-stochastic-optimization/
|
10.1609/aaai.v35i9.16959
| 485,498 | 1 |
https://scholar.google.com/scholar?cites=17751492399821469906&as_sdt=5,24&sciodt=0,24&hl=en
| 12 |
bytedance.com;gmail.com;bytedance.com;osu.edu;bit.edu.cn;bytedance.com
|
bytedance.com;gmail.com;bytedance.com;osu.edu;bit.edu.cn;bytedance.com
|
https://github.com/Xunpeng746/ACMo
| 6 |
0;1;0;2;3;0
|
Bytedance;Peking University;Ohio State University;Beijing Institute of Technology
|
AI Lab;;;
|
https://www.bytedance.com;http://www.pku.edu.cn;https://www.osu.edu;http://www.bit.edu.cn/
|
;Peking U;OSU;BIT
|
0;1;0;2;1;0
|
Shanghai;Beijing;Columbus
|
0;0;0;1;0;0
|
China;United States
|
|||
02233
|
ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization
|
main
|
Technical
|
The object of Weakly-supervised Temporal Action Localization (WS-TAL) is to localize all action instances in an untrimmed video with only video-level supervision. Due to the lack of frame-level annotations during training, current WS-TAL methods rely on attention mechanisms to localize the foreground snippets or frames that contribute to the video-level classification task. This strategy frequently confuse context with the actual action, in the localization result. Separating action and context is a core problem for precise WS-TAL, but it is very challenging and has been largely ignored in the literature. In this paper, we introduce an Action-Context Separation Network (ACSNet) that explicitly takes into account context for accurate action localization. It consists of two branches (i.e., the Foreground-Background branch and the Action-Context branch). The Foreground-Background branch first distinguishes foreground from background within the entire video while the Action-Context branch further separates the foreground as action and context. We associate video snippets with two latent components (i.e., a positive component and a negative component), and their different combinations can effectively characterize foreground, action and context. Furthermore, we introduce extended labels with auxiliary context categories to facilitate the learning of action-context separation. Experiments on THUMOS14 and ActivityNet v1.2/v1.3 datasets demonstrate the ACSNet outperforms existing state-of-the-art WS-TAL methods by a large margin.
|
Computer Vision II
|
Ziyi Liu; Le Wang; Qilin Zhang; Wei Tang; Junsong Yuan; Nanning Zheng; Gang Hua
|
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University; HERE Technologies; University of Illinois at Chicago; The State University of New York at Buffalo; Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University; Wormpex AI Research
|
https://cdn.aaai.org/ojs/16322/16322-13-19816-1-2-20210518.pdf
|
https://aaai.org/papers/02233-acsnet-action-context-separation-network-for-weakly-supervised-temporal-action-localization/
|
10.1609/aaai.v35i3.16322
| 512,511 | 86 |
https://scholar.google.com/scholar?cites=7060591315283696399&as_sdt=5,44&sciodt=0,44&hl=en
| 10 |
stu.xjtu.edu.cn; flewang;mail.xjtu.edu.cn;uic.edu;buffalo.edu; fsamqzhang;gmail.com
|
stu.xjtu.edu.cn; flewang;mail.xjtu.edu.cn;uic.edu;buffalo.edu; fsamqzhang;gmail.com
| 7 |
0;0;1;2;3;0;4
|
Xi'an Jiaotong University;HERE Technologies;University of Illinois at Chicago;State University of New York at Buffalo;Wormpex AI Research
|
Institute of Artificial Intelligence and Robotics;;;;AI Research
|
http://www.xjtu.edu.cn;https://www.here.com;https://www.uic.edu;https://www.buffalo.edu;
|
XJTU;HERE;UIC;SUNY Buffalo;Wormpex AI
|
0;0;2;3;0
|
Xi'an;;Chicago;Buffalo
|
0;0;1;2;2;0;2
|
China;Finland;United States
|
||||
13261
|
ACT: an Attentive Convolutional Transformer for Efficient Text Classification
|
main
|
Technical
|
Recently, Transformer has been demonstrating promising performance in many NLP tasks and showing a trend of replacing Recurrent Neural Network (RNN). Meanwhile, less attention is drawn to Convolutional Neural Network (CNN) due to its weak ability in capturing sequential and long-distance dependencies, although it has excellent local feature extraction capability. In this paper, we introduce an Attentive Convolutional Transformer (ACT) that takes the advantages of both Transformer and CNN for efficient text classification. Specifically, we propose a novel attentive convolution mechanism that utilizes the semantic meaning of convolutional filters attentively to transform text from complex word space to a more informative convolutional filter space where important n-grams are captured. ACT is able to capture both local and global dependencies effectively while preserving sequential information. Experiments on various text classification tasks and detailed analyses show that ACT is a lightweight, fast, and effective universal text classifier, outperforming CNNs, RNNs, and attentive models including Transformer.
|
Speech and Natural Language Processing II
|
Pengfei Li; Peixiang Zhong; Kezhi Mao; Dongzhe Wang; Xuefeng Yang; Yunfeng Liu; Jianxiong Yin; Simon See
|
Nanyang Technological University, Singapore; Nanyang Technological University, Singapore; Nanyang Technological University, Singapore; ZhuiYi Technology, Shenzhen, China; ZhuiYi Technology, Shenzhen, China; ZhuiYi Technology, Shenzhen, China; NVIDIA AI Tech Center; NVIDIA AI Tech Center
|
https://cdn.aaai.org/ojs/17566/17566-13-21060-1-2-20210518.pdf
|
https://aaai.org/papers/13261-act-an-attentive-convolutional-transformer-for-efficient-text-classification/
|
10.1609/aaai.v35i15.17566
| 619,767 | 56 |
https://scholar.google.com/scholar?cites=2472618928712927923&as_sdt=2005&sciodt=0,5&hl=en
| 5 |
ntu.edu.sg;ntu.edu.sg;ntu.edu.sg;wezhuiyi.com;wezhuiyi.com;wezhuiyi.com;nvidia.com;nvidia.com
|
ntu.edu.sg;ntu.edu.sg;ntu.edu.sg;wezhuiyi.com;wezhuiyi.com;wezhuiyi.com;nvidia.com;nvidia.com
| 8 |
0;0;0;1;1;1;2;2
|
Nanyang Technological University;ZhuiYi Technology;NVIDIA
|
;;NVIDIA AI Tech Center
|
https://www.ntu.edu.sg;;https://www.nvidia.com
|
NTU;;NVIDIA
|
0;0;0;1;1;1;2;2
|
Singapore;China;United States
|
||||||
10665
|
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
|
main
|
Technical
|
Incorporating second-order curvature information into machine learning optimization algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs associated with forming the Hessian and performing the associated linear system solve. To address this, we introduce ADAHESSIAN, a new stochastic optimization algorithm. ADAHESSIAN directly incorporates approximate curvature information from the loss function, and it includes several novel performance-improving features, including: (i) a fast Hutchinson based method to approximate the curvature matrix with low computational overhead; (ii) a spatial averaging to reduce the variance of the second derivative; and (iii) a root-mean-square exponential moving average to smooth out variations of the second-derivative across different iterations. We perform extensive tests on NLP, CV, and recommendation system tasks, and ADAHESSIAN achieves state-of-the-art results. In particular, we find that ADAHESSIAN: (i) outperforms AdamW for transformers by0.13/0.33 BLEU score on IWSLT14/WMT14, 2.7/1.0 PPLon PTB/Wikitext-103; (ii) outperforms AdamW for Squeeze-Bert by 0.41 points on GLUE; (iii) achieves 1.45%/5.55%higher accuracy on ResNet32/ResNet18 on Cifar10/ImageNetas compared to Adam; and (iv) achieves 0.032% better score than Adagrad for DLRM on the Criteo Ad Kaggle dataset. The cost per iteration of ADAHESSIANis comparable to first-order methods, and ADAHESSIAN exhibits improved robustness towards variations in hyperparameter values. The code for ADAHESSIAN is open-sourced and publicly-available [1].
|
Machine Learning V
|
Zhewei Yao; Amir Gholami; Sheng Shen; Mustafa Mustafa; Kurt Keutzer; Michael Mahoney
|
University of California, Berkeley; University of California, Berkeley; University of California, Berkeley; Lawrence Berkeley National Laboratory; University of California, Berkeley; University of California, Berkeley
|
https://cdn.aaai.org/ojs/17275/17275-13-20769-1-2-20210518.pdf
|
https://aaai.org/papers/10665-adahessian-an-adaptive-second-order-optimizer-for-machine-learning/
|
10.1609/aaai.v35i12.17275
| 792,430 | 322 |
https://scholar.google.com/scholar?cites=10009577297392129077&as_sdt=40005&sciodt=0,10&hl=en
| 9 |
berkeley.edu;berkeley.edu;berkeley.edu;lbl.gov;berkeley.edu;berkeley.edu
|
berkeley.edu;berkeley.edu;berkeley.edu;lbl.gov;berkeley.edu;berkeley.edu
| 6 |
0;0;0;1;0;0
|
University of California, Berkeley;Lawrence Berkeley National Laboratory
|
;
|
https://www.berkeley.edu;https://www.lbl.gov
|
UC Berkeley;LBL
|
0;0;0;0;0;0
|
Berkeley
|
0;0;0;0;0;0
|
United States
|
||||
05957
|
AI-Assisted Scientific Data Collection with Iterative Human Feedback
|
main
|
Technical
|
Although artificial intelligence has revolutionized data analysis, significantly less work has focused on using AI to improve scientific data collection. Past work in AI for data collection has typically assumed the objective function is well-defined by humans before starting an experiment; however, this is a poor fit for scientific domains where new discoveries and insights are made as data is being collected. In this paper we present a new framework to allow AI systems to work together with humans (e.g. scientists) to collect data more effectively in simple scientific domains. We present a novel algorithm, TESA, which seeks to achieve good performance by learning from past human behavior how to direct data to places that are likely to become scientifically interesting in the future. We analyze the problem theoretically, defining a novel notion of regret in this setting and showing that TESA is zero regret. Next, we show that TESA outperforms other related algorithms in simulations using real data drawn from three diverse domains (economics, mental health, and cognitive psychology). Finally, we run experiments with human subjects across these scientific domains to compare our iterative human-in-the-loop process to a (more standard) workflow in which information is communicated to the AI a priori.
|
Humans and AI
|
Travis Mandel; James Boyd; Sebastian J. Carter; Randall H. Tanaka; Taishi Nammoto
|
University of Hawai‘i at Hilo, Hilo, HI; University of Hawai‘i at Hilo, Hilo, HI; University of Hawai‘i at Hilo, Hilo, HI; University of Hawai‘i at Hilo, Hilo, HI; University of Hawai‘i at Hilo, Hilo, HI
|
https://cdn.aaai.org/ojs/16744/16744-13-20238-1-2-20210518.pdf
|
https://aaai.org/papers/05957-ai-assisted-scientific-data-collection-with-iterative-human-feedback/
|
10.1609/aaai.v35i7.16744
| 311,982 | 1 |
https://scholar.google.com/scholar?cites=18228925098488029199&as_sdt=400005&sciodt=0,14&hl=en
| 5 |
hawaii.edu;hawaii.edu;hawaii.edu;hawaii.edu;hawaii.edu
|
hawaii.edu;hawaii.edu;hawaii.edu;hawaii.edu;hawaii.edu
| 5 |
0;0;0;0;0
|
University of Hawai‘i at Hilo
|
https://www.hilo.hawaii.edu
|
UH Hilo
|
0;0;0;0;0
|
Hilo
|
0;0;0;0;0
|
United States
|
|||||
13657
|
ALP-KD: Attention-Based Layer Projection for Knowledge Distillation
|
main
|
Technical
|
Knowledge distillation is considered as a training and compression strategy in which two neural networks, namely a teacher and a student, are coupled together during training. The teacher network is supposed to be a trustworthy predictor and the student tries to mimic its predictions. Usually, a student with a lighter architecture is selected so we can achieve compression and yet deliver high-quality results. In such a setting, distillation only happens for final predictions whereas the student could also benefit from teacher’s supervision for internal components. Motivated by this, we studied the problem of distillation for intermediate layers. Since there might not be a one-to-one alignment between student and teacher layers, existing techniques skip some teacher layers and only distill from a subset of them. This shortcoming directly impacts quality, so we instead propose a combinatorial technique which relies on attention. Our model fuses teacher-side information and takes each layer’s significance into consideration, then it performs distillation between combined teacher layers and those of the student. Using our technique, we distilled a 12-layer BERT (Devlin et al. 2019) into 6-, 4-, and 2-layer counterparts and evaluated them on GLUE tasks (Wang et al. 2018). Experimental results show that our combinatorial approach is able to outperform other existing techniques.
|
Speech and Natural Language Processing II
|
Peyman Passban; Yimeng Wu; Mehdi Rezagholizadeh; Qun Liu
|
Amazon; Huawei Noah’s Ark Lab; Huawei Noah’s Ark Lab; Huawei Noah’s Ark Lab
|
https://cdn.aaai.org/ojs/17610/17610-13-21104-1-2-20210518.pdf
|
https://aaai.org/papers/13657-alp-kd-attention-based-layer-projection-for-knowledge-distillation/
|
10.1609/aaai.v35i15.17610
| 330,100 | 135 |
https://scholar.google.com/scholar?cites=7274583512030520110&as_sdt=40005&sciodt=0,10&hl=en
| 4 |
gmail.com;huawei.com;huawei.com;huawei.com
|
gmail.com;huawei.com;huawei.com;huawei.com
| 4 |
0;1;1;1
|
Amazon.com, Inc.;Huawei
|
;Noah’s Ark Lab
|
https://www.amazon.com;https://www.huawei.com
|
Amazon;Huawei
|
0;1;1;1
|
United States;China
|
||||||
03625
|
ASHF-Net: Adaptive Sampling and Hierarchical Folding Network for Robust Point Cloud Completion
|
main
|
Technical
|
Estimating the complete 3D point cloud from an incomplete one lies at the core of many vision and robotics applications. Existing methods typically predict the complete point cloud based on the global shape representation extracted from the incomplete input. Although they could predict the overall shape of 3D objects, they are incapable of generating structure details of objects. Moreover, the partial input point sets obtained from range scans are often sparse, noisy and non-uniform, which largely hinder shape completion. In this paper, we propose an adaptive sampling and hierarchical folding network (ASHF-Net) for robust 3D point cloud completion. Our main contributions are two-fold. First, we propose a denoising auto-encoder with an adaptive sampling module, aiming at learning robust local region features that are insensitive to noise. Second, we propose a hierarchical folding decoder with the gated skip-attention and multi-resolution completion goal to effectively exploit the local structure details of partial inputs. We also design a KL regularization term to evenly distribute the generated points. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on multiple 3D point cloud completion benchmarks.
|
Computer Vision III
|
Daoming Zong; Shiliang Sun; Jing Zhao
|
School of Computer Science and Technology, East China Normal University, Shanghai, China; School of Computer Science and Technology, East China Normal University, Shanghai, China; School of Computer Science and Technology, East China Normal University, Shanghai, China
|
https://cdn.aaai.org/ojs/16478/16478-13-19972-1-2-20210518.pdf
|
https://aaai.org/papers/03625-ashf-net-adaptive-sampling-and-hierarchical-folding-network-for-robust-point-cloud-completion/
|
10.1609/aaai.v35i4.16478
| 8,832,252 | 26 |
https://scholar.google.com/scholar?cites=4623980687056496746&as_sdt=400005&sciodt=0,14&hl=en
| 4 |
gmail.com;cs.ecnu.edu.cn;cs.ecnu.edu.cn
|
gmail.com;cs.ecnu.edu.cn;cs.ecnu.edu.cn
| 3 |
0;0;0
|
East China Normal University
|
School of Computer Science and Technology
|
http://www.ecnu.edu.cn
|
ECNU
|
0;0;0
|
Shanghai
|
0;0;0
|
China
|
End of preview.
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
Paper Lists
This repository powers Paper Copilot, combining data from multiple sources to ensure coherence, consistency, and comprehensiveness.
Typically, records from OpenReview, official conference sources, or open access sites are scattered, leading to fragmented information and extra effort to navigate between them. The aim of this repository is to serve as a comprehensive link collection for major conferences, enabling easier access to relevant information, and statistical analysis will be based on these records.
Local Search Tool
We further provide a streamlit-based tool for efficiently searching and analyzing conference papers locally. Thanks to @hhh2210's contribution.
Setup
# Clone the repo and install dependencies
git clone https://github.com/papercopilot/paperlists.git
# use conda if needed: conda create -n papercopilot python=3.10
pip install -r requirements.txt
Usage
1. Web Interface
cd paperlists/tools
streamlit run app.py
# a corresponding local url will popsup, e.g. `Local URL: http://localhost:8501`
2. Command Line Usage
cd paperlists/tools
python extract.py [keyword] [-i INPUT_PATH] [-o OUTPUT_FILE] [-f FIELDS...]
keyword: Search keyword (required)-i, --input_path: Input JSON file or directory (default: iclr2025.json)-o, --output_file: Output JSON file (optional)-f, --fields: Fields to search (default: keywords title primary_area topic)
Example:
cd paperlists/tools
python extract.py retrieval -i iclr/iclr2025.json -o results.json -f title keywords
Overview
ICLR
| Year | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| json | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
| Statistics (Main) | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
| Year | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 |
|---|---|---|---|---|---|---|---|
| json | 2019 | 2018 | 2017 | 2014 | 2013 | ||
| Statistics (Main) | 2019 | 2018 | 2017 | 2014 | 2013 |
NeurIPS(NIPS)
| Year | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| json | 2024 | 2023 | 2022 | 2021 | 2020 |
| Statistics (Main) | 2024 | 2023 | 2022 | 2021 | |
| Statistics (Datasets & Benchmarks) | 2024 | 2023 | 2022 |
| Year | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 |
|---|---|---|---|---|---|---|---|---|---|---|
| json | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 |
| Year | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 |
|---|---|---|---|---|---|---|---|---|---|---|
| json | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 |
ICML
SIGGRAPH
| Year | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 |
|---|---|---|---|---|---|---|---|---|---|---|
| json | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 |
| Paperlist | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 |
SIGGRAPH Asia
| Year | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 |
|---|---|---|---|---|---|---|---|
| json | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 |
| Paperlist | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 |
CVPR
ICCV [Coming Soon]
ECCV [Coming Soon]
EMNLP
CoRL
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