陈大鑫作者

NeurIPS 今年共收录1900篇论文,我该怎么阅读?

近日,知乎上有个小热的问题:

在这个问题下,已经有众多大佬对如何阅读论文进行献言献策。

确实,今年 NeurIPS 2020 有接近两千篇论文被接收,这是一个什么概念?

据说,AI 圈子的一位大神——旷视科技张祥雨博士,3 年看完了 1800 篇论文。

这已经是相当恐怖的速度了,按照这个速度,对大神而言,读完 NeurIPS 2020 的论文尚且需要花费三年的时间,这让别人该何去何从?

关于如何读论文,AI 科技评论之前也有一篇“吴恩达教你读论文:持续而缓慢的学习,才是正道”的文章 ,大家可以再次阅读学习。

按照吴恩达的观点,读论文不能贪快,要高质量、持续地阅读学习才是正道。

今日,AI 科技评论以 NeurIPS 2020 接近两千篇的论文为例,给大家提供两个论文阅读的便利。

1、阅读大牛的论文:

见“ NeurIPS 2020 论文接收大排行!谷歌 169 篇第一、斯坦福第二、清华国内第一”一文。

在这篇文章中,AI 科技评论列举了 AI 学术大牛如深度学习三巨头、周志华李飞飞等人的论文,大牛的团队出品的论文,质量平均而言肯定有很大保证的。

2、按主题分门别类的阅读:

这是显而易见的选择,也是大家正在做的事情,AI 科技评论今天这篇文章正是把 NeurIPS 2020 的论文做了一个简单分类统计供大家参考阅读。

说明:

1、统计主题根据日常经常接触到的i进行,不保证全面。

2、统计会有交叉和重复:如论文《Semi-Supervised Neural Architecture Search》会被半监督学习和 NAS 统计两次。

3、统计基于“人工”(的)智能,若有疏漏和错误请怪在 AI 身上。

4、本文统计后续补充会持续更新在 AI 科技评论知乎专栏上,欢迎大家关注。


前奏

1、论文题目最短的论文:

《Choice Bandits》

2、合作人数最多(31人)的论文:谷歌大脑+OpenAI 29人+约翰霍普斯金天团

《Language Models are Few-Shot Learners 》 

3、模仿 Attenton is all you need?

4、五篇和新冠肺炎有关的论文:

《何时以及如何解除风险?基于区域高斯过程的全球 COVID-19(新冠肺炎)情景分析与政策评估》

《新冠肺炎在德国传播的原因分析》 

《CogMol:新冠肺炎靶向性和选择性药物设计》

《非药物干预对新冠肺炎传播有效性估计的鲁棒性研究》

《新冠肺炎预测的可解释序列学习》

另附:COVID-19 Open Data:新冠疫情开放时序数据集 https://github.com/GoogleCloudPlatform/covid-19-open-data

5、五篇 Rethinking 的文章:

《重新思考标签对改善类不平衡学习的价值》

《重新思考预训练和自训练》

这篇由谷歌大脑出品的论文 6 月 11 日就挂在 arXiv 上面了,

论文链接:https://arxiv.org/pdf/2006.06882

《重新思考图神经网络的池化层》

《重新思考通用特征转换中可学习树 Filter》

《重新思考分布转移/转换下深度学习的重要性权重 》


6、题目带有 Beyond 的论文:

今年 ACL 2020 最佳论文题目正是带有 Beyond 一词,以下论文中的某一篇说不定会沾沾 ACL 2020 最佳论文的喜气在 NeurIPS 2020 上面获个大奖。(如未获奖,概不负责)


其中第一篇论文以 Beyond accuracy 开头,这和 ACL 2020 最佳论文题目开头一模一样了。

7、题目比较有意思的论文:

《Teaching a GAN What Not to Learn》
Siddarth Asokan (Indian Institute of Science) · Chandra Seelamantula (IISc Bangalore)

《Self-supervised learning through the eyes of a child》
Emin Orhan (New York University) · Vaibhav Gupta (New York University) · Brenden Lake (New York University)

《How hard is to distinguish graphs with graph neural networks?》
Andreas Loukas (EPFL)

《Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding》

Rishi S Sonthalia (University of Michigan) · Anna Gilbert (University of Michigan)

8、Relu:7 篇


2

NLP相关


1、BERT:7 篇



2、Attention:24 篇,这里 Attention 不止有用在 NLP 领域,这里暂且归到NLP 分类下,下同。

1、Auto Learning Attention

Benteng Ma (Northwestern Polytechnical University) · Jing Zhang (The University of Sydney) · Yong Xia (Northwestern Polytechnical University, Research & Development Institute of Northwestern Polytechnical University in Shenzhen) · Dacheng Tao (University of Sydney)

2、Bayesian Attention Modules

Xinjie Fan (UT Austin) · Shujian Zhang (UT Austin) · Bo Chen (Xidian University) · Mingyuan Zhou (University of Texas at Austin)

3、Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention

Ekta Sood (University of Stuttgart, Simtech ) · Simon Tannert (Institute for Natural Language Processing, University of Stuttgart) · Philipp Mueller (VIS, University of Stuttgart) · Andreas Bulling (University of Stuttgart)

4、Prophet Attention: Predicting Attention with Future Attention for Improved Image Captioning

Fenglin Liu (Peking University) · Xuancheng Ren (Peking University) · Xian Wu (Tencent Medical AI Lab) · Shen Ge (Tencent Medical AI Lab) · Wei Fan (Tencent) · Yuexian Zou (Peking University) · Xu Sun (Peking University)

5、Kalman Filtering Attention for User Behavior Modeling in CTR Prediction

Hu Liu (JD.com) · Jing LU (Business Growth BU JD.com) · Xiwei Zhao (JD.com) · Sulong Xu (JD.com) · Hao Peng (JD.com) · Yutong Liu (JD.com) · Zehua Zhang (JD.com) · Jian Li (JD.com) · Junsheng Jin (JD.com) · Yongjun Bao (JD.com) · Weipeng Yan (JD.com)

6、RANet: Region Attention Network for Semantic Segmentation

Dingguo Shen (Shenzhen University) · Yuanfeng Ji (City University of Hong Kong) · Ping Li (The Hong Kong Polytechnic University) · Yi Wang (Shenzhen University) · Di Lin (Tianjin University)

7、SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Fabian Fuchs (University of Oxford) · Daniel Worrall (University of Amsterdam) · Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence) · Max Welling (University of Amsterdam / Qualcomm AI Research)

8、Complementary Attention Self-Distillation for Weakly-Supervised Object Detection

Zeyi Huang (carnegie mellon university) · Yang Zou (Carnegie Mellon University) · B. V. K. Vijaya Kumar (CMU, USA) · Dong Huang (Carnegie Mellon University)

9、Modern Hopfield Networks and Attention for Immune Repertoire Classification

Michael Widrich (LIT AI Lab / University Linz) · Bernhard Schäfl (JKU Linz) · Milena Pavlović (Department of Informatics, University of Oslo) · Hubert Ramsauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) · Lukas Gruber (Johannes Kepler University) · Markus Holzleitner (LIT AI Lab / University Linz) · Johannes Brandstetter (LIT AI Lab / University Linz) · Geir Kjetil Sandve (Department of Informatics, University of Oslo) · Victor Greiff (Department of Immunology, University of Oslo) · Sepp Hochreiter (LIT AI Lab / University Linz / IARAI) · Günter Klambauer (LIT AI Lab / University Linz)

10、Untangling tradeoffs between recurrence and self-attention in artificial neural networks

Giancarlo Kerg (MILA) · Bhargav Kanuparthi (Montreal Institute for Learning Algorithms) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Kyle Goyette (University of Montreal) · Yoshua Bengio (Mila / U. Montreal) · Guillaume Lajoie (Mila, Université de Montréal)

11、RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

Riccardo Del Chiaro (University of Florence) · Bartłomiej Twardowski (Computer Vision Center, UAB) · Andrew D Bagdanov (University of Florence) · Joost van de Weijer (Computer Vision Center Barcelona)

12、Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement

Xin Liu (University of Washington ) · Josh Fromm (OctoML) · Shwetak Patel (University of Washington) · Daniel McDuff (Microsoft Research)

13、SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection

Xiaoya Li (Shannon.AI) · Yuxian Meng (Shannon.AI) · Mingxin Zhou (Shannon.AI) · Qinghong Han (Shannon.AI) · Fei Wu (Zhejiang University) · Jiwei Li (Shannon.AI)

14、Fast Transformers with Clustered Attention

Apoorv Vyas (Idiap Research Institute) · Angelos Katharopoulos (Idiap) · François Fleuret (University of Geneva)

15、Sparse and Continuous Attention Mechanisms

André Martins () · Marcos Treviso (Instituto de Telecomunicacoes) · António Farinhas (Instituto Superior Técnico) · Vlad Niculae (Instituto de Telecomunicações) · Mario Figueiredo (University of Lisbon) · Pedro Aguiar (Instituto Superior Técnico)

16、Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

David Bieber (Google Brain) · Charles Sutton (Google) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

17、Neural encoding with visual attention

Meenakshi Khosla (Cornell University) · Gia Ngo (Cornell University) · Keith Jamison (Cornell University) · Amy Kuceyeski (Cornell University) · Mert Sabuncu (Cornell)

18、Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

Yunqiu Xu (University of Technology Sydney) · Meng Fang (Tencent) · Ling Chen (" University of Technology, Sydney, Australia") · Yali Du (University College London) · Joey Tianyi Zhou (IHPC, A*STAR) · Chengqi Zhang (University of Technology Sydney)

19、Object-Centric Learning with Slot Attention

Francesco Locatello (ETH Zürich - MPI Tübingen) · Dirk Weissenborn (Google) · Thomas Unterthiner (Google Research, Brain Team) · Aravindh Mahendran (Google) · Georg Heigold (Google) · Jakob Uszkoreit (Google, Inc.) · Alexey Dosovitskiy (Google Research) · Thomas Kipf (Google Research)

20、SMYRF - Efficient attention using asymmetric clustering

Giannis Daras (National Technical University of Athens) · Nikita Kitaev (University of California, Berkeley) · Augustus Odena (Google Brain) · Alexandros Dimakis (University of Texas, Austin)

21、Focus of Attention Improves Information Transfer in Visual Features

Matteo Tiezzi (University of Siena) · Stefano Melacci (University of Siena) · Alessandro Betti (University of Siena) · Marco Maggini (University of Siena) · Marco Gori (University of Siena)

22、AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

Afshin Oroojlooy (SAS Institute, Inc) · Mohammadreza Nazari (SAS Institute Inc.) · Davood Hajinezhad (SAS Institute Inc.) · Jorge Silva (SAS)

23、Multi-agent Trajectory Prediction with Fuzzy Query Attention

Nitin Kamra (University of Southern California) · Hao Zhu (Peking University) · Dweep Kumarbhai Trivedi (University of Southern California) · Ming Zhang (Peking University) · Yan Liu (University of Southern California)

24、Limits to Depth Efficiencies of Self-Attention

Yoav Levine (HUJI) · Noam Wies (Hebrew University of Jerusalem) · Or Sharir (Hebrew University of Jerusalem) · Hofit Bata (Hebrew University of Jerusalem) · Amnon Shashua (Hebrew University of Jerusalem)

25、Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention

Ekta Sood (University of Stuttgart, Simtech ) · Simon Tannert (Institute for Natural Language Processing, University of Stuttgart) · Philipp Mueller (VIS, University of Stuttgart) · Andreas Bulling (University of Stuttgart

3、Transformer:14 篇

1、Fast Transformers with Clustered Attention

Apoorv Vyas (Idiap Research Institute) · Angelos Katharopoulos (Idiap)· François Fleuret (University of Geneva)

2、Deep Transformers with Latent Depth

Xian Li (Facebook) · Asa Cooper Stickland (University of Edinburgh) · Yuqing Tang (Facebook AI) · Xiang Kong (Carnegie Mellon University)

3、CrossTransformers: spatially-aware few-shot transfer

Carl Doersch (DeepMind) · Ankush Gupta (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

4、SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Fabian Fuchs (University of Oxford) · Daniel Worrall (University of Amsterdam) · Volker Fischer (Robert Bosch GmbH, Bosch Center for Artificial Intelligence) · Max Welling (University of Amsterdam / Qualcomm AI Research)

5、Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

Zihang Dai (Carnegie Mellon University) · Guokun Lai (Carnegie Mellon University) · Yiming Yang (CMU) · Quoc V Le (Google)

6、Adversarial Sparse Transformer for Time Series Forecasting

Sifan Wu (Tsinghua University) · Xi Xiao (Tsinghua University) · Qianggang Ding (Tsinghua University) · Peilin Zhao (Tencent AI Lab) · Ying Wei (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

7、Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping

Minjia Zhang (Microsoft) · Yuxiong He (Microsoft)

8、COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

Mohammadreza Zolfaghari (University of Freiburg) · Simon Ging (Uni Freiburg) · Hamed Pirsiavash (University of Maryland, Baltimore County) · Thomas Brox (University of Freiburg)

9、Cascaded Text Generation with Markov Transformers

Yuntian Deng (Harvard University) · Alexander Rush (Cornell University)

10、GROVER: Self-Supervised Message Passing Transformer on Large-scale Molecular Graphs

Yu Rong (Tencent AI Lab) · Yatao Bian (Tencent AI Lab) · Tingyang Xu (Tencent AI Lab) · Weiyang Xie (Tencent AI Lab) · Ying WEI (Tencent AI Lab) · Wenbing Huang (Tsinghua University) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

11、Learning to Communicate in Multi-Agent Systems via Transformer-Guided Program Synthesis

Jeevana Priya Inala (MIT) · Yichen Yang (MIT) · James Paulos (University of Pennsylvania) · Yewen Pu (MIT) · Osbert Bastani (University of Pennysylvania) · Vijay Kumar (University of Pennsylvania) · Martin Rinard (MIT) · Armando Solar-Lezama (MIT)

12、Measuring Systematic Generalization in Neural Proof Generation with Transformers

Nicolas Gontier (Mila, Polytechnique Montréal) · Koustuv Sinha (McGill University / Mila / FAIR) · Siva Reddy (McGill University) · Chris Pal (Montreal Institute for Learning Algorithms, École Polytechnique, Université de Montréal)

13、O(n)  Connections are Expressive Enough: Universal Approximability of Sparse Transformers

Chulhee Yun (MIT) · Yin-Wen Chang (Google Inc.) · Srinadh Bhojanapalli (Google AI) · Ankit Singh Rawat (Google Research) · Sashank Reddi (Google) · Sanjiv Kumar (Google Research)

14、MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wenhui Wang (MSRA) · Furu Wei (Microsoft Research Asia) · Li Dong (Microsoft Research) · Hangbo Bao (Harbin Institute of Technology) · Nan Yang (Microsoft Research Asia) · Ming Zhou (Microsoft Research)

4、预训练:5 篇

1、Pre-training via Paraphrasing

Mike Lewis (Facebook AI Research) · Marjan Ghazvininejad (Facebook AI Research) · Gargi Ghosh (Facebook) · Armen Aghajanyan (Facebook) · Sida Wang (Facebook AI Research) · Luke Zettlemoyer (University of Washington and Allen Institute for Artificial Intelligence)

2、Pre-Training Graph Neural Networks: A Contrastive Learning Framework with Augmentations

Yuning You (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Yongduo Sui (University of Science and Technology of China) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin) · Yang Shen (Texas A&M University)

3、Rethinking Pre-training and Self-training

Barret Zoph (Google Brain) · Golnaz Ghiasi (Google) · Tsung-Yi Lin (Google Brain) · Yin Cui (Google) · Hanxiao Liu (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Quoc V Le (Google)

4、MPNet: Masked and Permuted Pre-training for Language Understanding

Kaitao Song (Nanjing University of Science and technology) · Xu Tan (Microsoft Research) · Tao Qin (Microsoft Research) · Jianfeng Lu (Nanjing University of Science and Technology) · Tie-Yan Liu (Microsoft Research Asia)

5、Adversarial Contrastive Learning: Harvesting More Robustness from Unsupervised Pre-Training

Ziyu Jiang (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin)

3


CV相关目标检测:12 篇

1、A Ranking-based, Balanced Loss Function for Both Classification and Localisation in Object Detection

Kemal Oksuz (Middle East Technical University) · Baris Can Cam (Roketsan) · Emre Akbas (Middle East Technical University) · Sinan Kalkan (Middle East Technical University)

2、UWSOD: Toward Fully-Supervised-Level Performance Weakly SupervisedObject Detection

Yunhang Shen (Xiamen University) · Rongrong Ji (Xiamen University, China) · Zhiwei Chen (Xiamen University) · Yongjian Wu (Tencent Technology (Shanghai) Co.,Ltd) · Feiyue Huang (Tencent)

3、Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection

Xiang Li (NJUST) · Wenhai Wang (Nanjing University) · Lijun Wu (Sun Yat-sen University) · Shuo Chen (Nanjing University of Science and Technology) · Xiaolin Hu (Tsinghua University) · Jun Li (Nanjing University of Science and Technology) · Jinhui Tang (Nanjing University of Science and Technology) · Jian Yang (Nanjing University of Science and Technology)

4、Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization

Qi Chen (Johns Hopkins University) · Lin Sun (Samsung, Stanford, HKUST) · Ernest Cheung (Samsung) · Alan Yuille (Johns Hopkins University)

5、Complementary Attention Self-Distillation for Weakly-Supervised Object Detection

Zeyi Huang (carnegie mellon university) · Yang Zou (Carnegie Mellon University) · B. V. K. Vijaya Kumar (CMU, USA) · Dong Huang (Carnegie Mellon University)

6、Few-Cost Salient Object Detection with Adversarial-Paced Learning

Dingwen Zhang (Xidian University) · HaiBin Tian (Xidian University) · Jungong Han (University of Warwick)

7、Bridging Visual Representations for Object Detection

Cheng Chi (University of Chinese Academy of Sciences) · Fangyun Wei (Microsoft Research Asia) · Han Hu (Microsoft Research Asia)

8、Fine-Grained Dynamic Head for Object Detection

Lin Song (Xi'an Jiaotong University) · Yanwei Li (The Chinese University of Hong Kong) · Zhengkai Jiang (Institute of Automation,Chinese Academy of Sciences) · Zeming Li (Megvii(Face++) Inc) · Hongbin Sun (Xi'an Jiaotong University) · Jian Sun (Megvii, Face++) · Nanning Zheng (Xi'an Jiaotong University)

9、Detection as Regression: Certified Object Detection with Median Smoothing

Ping-yeh Chiang (University of Maryland, College Park) · Michael Curry (University of Maryland) · Ahmed Abdelkader (University of Maryland, College Park) · Aounon Kumar (University of Maryland, College Park) · John Dickerson (University of Maryland) · Tom Goldstein (University of Maryland)

10、RepPoints v2: Verification Meets Regression for Object Detection

Yihong Chen (Peking University) · Zheng Zhang (MSRA) · Yue Cao (Microsoft Research) · Liwei Wang (Peking University) · Stephen Lin (Microsoft Research) · Han Hu (Microsoft Research Asia)

11、CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection

Qijian Zhang (City University of Hong Kong) · Runmin Cong (Beijing Jiaotong University) · Junhui Hou (City University of Hong Kong, Hong Kong) · Chongyi Li ( Nanyang Technological University) · Yao Zhao (Beijing Jiaotong University)

12、Restoring Negative Information in Few-Shot Object Detection

Yukuan Yang (Tsinghua University) · Fangyun Wei (Microsoft Research Asia) · Miaojing Shi (King's College London) · Guoqi Li (Tsinghua University)

目标分割:3篇

1、Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement

Yongqing Liang (Louisiana State University) · Xin Li (Louisiana State University) · Navid Jafari (Louisiana State University) · Jim Chen (Northeastern University)


2、Make One-Shot Video Object Segmentation Efficient Again


Tim Meinhardt (TUM) · Laura Leal-Taixé (TUM)


3、Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation


Yuxi Li (Shanghai Jiao Tong University) · Jinlong Peng (Tencent Youtu Lab) · Ning Xu (Adobe Research) · John See (Multimedia University) · Weiyao Lin (Shanghai Jiao Tong university)


实例分割:2 篇

1、Deep Variational Instance Segmentation

Jialin Yuan (Oregon State University) · Chao Chen (Stony Brook University) · Fuxin Li (Oregon State University)

2、DFIS: Dynamic and Fast Instance Segmentation

Xinlong Wang (University of Adelaide) · Rufeng Zhang (Tongji University) · Tao Kong (Bytedance) · Lei Li (ByteDance AI Lab) · Chunhua Shen (University of Adelaide)

行人重识别:

4

各种Learning

1、强化学习:94 篇

1、Reinforcement Learning for Control with Multiple Frequencies

Jongmin Lee (KAIST) · ByungJun Lee (KAIST) · Kee-Eung Kim (KAIST)

2、Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension

Ruosong Wang (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Lin Yang (UCLA)

3、Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting

Ziping Xu (University of Michigan) · Ambuj Tewari (University of Michigan)

4、Reinforcement Learning with Feedback Graphs

Christoph Dann (Carnegie Mellon University) · Yishay Mansour (Google) · Mehryar Mohri (Courant Inst. of Math. Sciences & Google Research) · Ayush Sekhari (Cornell University) · Karthik Sridharan (Cornell University)

5、Reinforcement Learning with Augmented Data

Misha Laskin (UC Berkeley) · Kimin Lee (UC Berkeley) · Adam Stooke (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai) · Aravind Srinivas (UC Berkeley)

6、Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Arthur Delarue (MIT) · Ross Anderson (Google Research) · Christian Tjandraatmadja (Google)

7、Breaking the Sample Size Barrier in Model-Based Reinforcement Learningwith a Generative Model

Gen Li (Tsinghua University) · Yuting Wei (Carnegie Mellon University) · Yuejie Chi (CMU) · Yuantao Gu (Tsinghua University) · Yuxin Chen (Princeton University)

8、Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

Zihan Zhang (Tsinghua University) · Yuan Zhou (UIUC) · Xiangyang Ji (Tsinghua University)

9、Effective Diversity in Population Based Reinforcement Learning

Jack Parker-Holder (University of Oxford) · Aldo Pacchiano (UC Berkeley) · Krzysztof M Choromanski (Google Brain Robotics) · Stephen J Roberts (University of Oxford)

10、A Boolean Task Algebra for Reinforcement Learning

Geraud Nangue Tasse (University of the Witwatersrand) · Steven James (University of the Witwatersrand) · Benjamin Rosman (University of the Witwatersrand / CSIR)

11、Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

Zhiyuan Xu (Syracuse University) · Kun Wu (Syracuse University) · Zhengping Che (DiDi AI Labs, Didi Chuxing) · Jian Tang (DiDi AI Labs, DiDi Chuxing) · Jieping Ye (Didi Chuxing)

12、Multi-task Batch Reinforcement Learning with Metric Learning

Jiachen Li (University of California, San Diego) · Quan Vuong (University of California San Diego) · Shuang Liu (University of California, San Diego) · Minghua Liu (UCSD) · Kamil Ciosek (Microsoft) · Henrik Christensen (UC San Diego) · Hao Su (UCSD)

13、On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems

Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · Bin Hu (University of Illinois at Urbana-Champaign) · Tamer Basar (University of Illinois at Urbana-Champaign)

14、Towards Playing Full MOBA Games with Deep Reinforcement Learning

Deheng Ye (Tencent) · Guibin Chen (Tencent) · Wen Zhang (Tencent) · chen sheng (qq) · Bo Yuan (Tencent) · Bo Liu (Tencent) · Jia Chen (Tencent) · Hongsheng Yu (Tencent) · Zhao Liu (Tencent) · Fuhao Qiu (Tencent AI Lab) · Liang Wang (Tencent) · Tengfei Shi (Tencent) · Yinyuting Yin (Tencent) · Bei Shi (Tencent AI Lab) · Lanxiao Huang (Tencent) · qiang fu (Tencent AI Lab) · Wei Yang (Tencent AI Lab) · Wei Liu (Tencent AI Lab)

15、Promoting Coordination through Policy Regularization in Multi-AgentDeep Reinforcement Learning

Julien Roy (Mila) · Paul Barde (Quebec AI institute - Ubisoft La Forge) · Félix G Harvey (Polytechnique Montréal) · Derek Nowrouzezahrai (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI)

16、Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning

Nathan Kallus (Cornell University) · Angela Zhou (Cornell University)

17、Learning Retrospective Knowledge with Reverse Reinforcement Learning

Shangtong Zhang (University of Oxford) · Vivek Veeriah (University of Michigan) · Shimon Whiteson (University of Oxford)

18、Combining Deep Reinforcement Learning and Search for Imperfect-Information Games

Noam Brown (Facebook AI Research) · Anton Bakhtin (Facebook AI Research) · Adam Lerer (Facebook AI Research) · Qucheng Gong (Facebook AI Research)

19、POMO: Policy Optimization with Multiple Optima for Reinforcement Learning

Yeong-Dae Kwon (Samsung SDS) · Jinho Choo (Samsung SDS) · Byoungjip Kim (Samsung SDS) · Iljoo Yoon (Samsung SDS) · Youngjune Gwon (Samsung SDS) · Seungjai Min (Samsung SDS)

20、Self-Paced Deep Reinforcement Learning

Pascal Klink (TU Darmstadt) · Carlo D'Eramo (TU Darmstadt) · Jan Peters (TU Darmstadt & MPI Intelligent Systems) · Joni Pajarinen (TU Darmstadt)

21、Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

Sebastian Curi (ETHz) · Felix Berkenkamp (Bosch Center for Artificial Intelligence) · Andreas Krause (ETH Zurich)

22、Weakly-Supervised Reinforcement Learning for Controllable Behavior

Lisa Lee (CMU / Google Brain / Stanford) · Ben Eysenbach (Carnegie Mellon University) · Russ Salakhutdinov (Carnegie Mellon University) · Shixiang (Shane) Gu (Google Brain) · Chelsea Finn (Stanford)

23、MOReL: Model-Based Offline Reinforcement Learning

Rahul Kidambi (Cornell University) · Aravind Rajeswaran (University of Washington) · Praneeth Netrapalli (Microsoft Research) · Thorsten Joachims (Cornell)

24、Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms

Pinar Ozisik (UMass Amherst) · Philip Thomas (University of Massachusetts Amherst)

25、Model-based Adversarial Meta-Reinforcement Learning

Zichuan Lin (Tsinghua University) · Garrett W. Thomas (Stanford University) · Guangwen Yang (Tsinghua University) · Tengyu Ma (Stanford University)

26、Safe Reinforcement Learning via Curriculum Induction

Matteo Turchetta (ETH Zurich) · Andrey Kolobov (Microsoft Research) · Shital Shah (Microsoft) · Andreas Krause (ETH Zurich) · Alekh Agarwal (Microsoft Research)

27、Conservative Q-Learning for Offline Reinforcement Learning

Aviral Kumar (UC Berkeley) · Aurick Zhou (University of California, Berkeley) · George Tucker (Google Brain) · Sergey Levine (UC Berkeley)

28、Munchausen Reinforcement Learning

Nino Vieillard (Google Brain) · Olivier Pietquin (Google Research Brain Team) · Matthieu Geist (Google Brain)

29、Non-Crossing Quantile Regression for Distributional Reinforcement Learning

Fan Zhou (Shanghai University of Finance and Economics) · Jianing Wang (Shanghai University of Finance and Economics) · Xingdong Feng (Shanghai University of Finance and Economics)

30、Online Decision Based Visual Tracking via Reinforcement Learning

ke Song (Shandong university) · Wei Zhang (Shandong University) · Ran Song (School of Control Science and Engineering, Shandong University) · Yibin Li (Shandong University)

31、Discovering Reinforcement Learning Algorithms

Junhyuk Oh (DeepMind) · Matteo Hessel (Google DeepMind) · Wojciech Czarnecki (DeepMind) · Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

32、Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Filippos Christianos (University of Edinburgh) · Lukas Schäfer (University of Edinburgh) · Stefano Albrecht (University of Edinburgh)

33、The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior inReinforcement Learning

Harm Van Seijen (Microsoft Research) · Hadi Nekoei (MILA) · Evan Racah (Mila, Université de Montréal) · Sarath Chandar (Mila / École Polytechnique de Montréal)

34、Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

Nino Vieillard (Google Brain) · Tadashi Kozuno (Okinawa Institute of Science and Technology) · Bruno Scherrer (INRIA) · Olivier Pietquin (Google Research Brain Team) · Remi Munos (DeepMind) · Matthieu Geist (Google Brain)

35、Task-agnostic Exploration in Reinforcement Learning

Xuezhou Zhang (UW-Madison) · Yuzhe Ma (University of Wisconsin-Madison) · Adish Singla (MPI-SWS)

36、Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Tianren Zhang (Tsinghua University) · Shangqi Guo (Tsinghua University) · Tian Tan (Stanford University) · Xiaolin Hu (Tsinghua University) · Feng Chen (Tsinghua University)

37、Storage Efficient and Dynamic Flexible Runtime Channel Pruning via DeepReinforcement Learning

Jianda Chen (Nanyang Technological University) · Shangyu Chen (Nanyang Technological University, Singapore) · Sinno Jialin Pan (Nanyang Technological University, Singapore)

38、Multi-Task Reinforcement Learning with Soft Modularization

Ruihan Yang (UC San Diego) · Huazhe Xu (UC Berkeley) · YI WU (UC Berkeley) · Xiaolong Wang (UCSD/UC Berkeley)

39、Weighted QMIX: Improving Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid (University of Oxford) · Gregory Farquhar (University of Oxford) · Bei Peng (University of Oxford) · Shimon Whiteson (University of Oxford)

40、MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

Elise van der Pol (University of Amsterdam) · Daniel Worrall (University of Amsterdam) · Herke van Hoof (University of Amsterdam) · Frans Oliehoek (TU Delft) · Max Welling (University of Amsterdam / Qualcomm AI Research)

41、On Efficiency in Hierarchical Reinforcement Learning

Zheng Wen (DeepMind) · Doina Precup (DeepMind) · Morteza Ibrahimi (DeepMind) · Andre Barreto (DeepMind) · Benjamin Van Roy (Stanford University) · Satinder Singh (DeepMind)

42、Variational Policy Gradient Method for Reinforcement Learning with General Utilities

Junyu Zhang (Princeton University) · Alec Koppel (U.S. Army Research Laboratory) · Amrit Singh Bedi (US Army Research Laboratory) · Csaba Szepesvari (DeepMind / University of Alberta) · Mengdi Wang (Princeton University)

43、Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Jianzhun Du (Harvard University) · Joseph Futoma (Harvard University) · Finale Doshi-Velez (Harvard)

44、DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Aviral Kumar (UC Berkeley) · Abhishek Gupta (University of California, Berkeley) · Sergey Levine (UC Berkeley)

45、Neurosymbolic Reinforcement Learning with Formally Verified Exploration

Greg Anderson (University of Texas at Austin) · Abhinav Verma (Rice University) · Isil Dillig (UT Austin) · Swarat Chaudhuri (The University of Texas at Austin)

46、Generalized Hindsight for Reinforcement Learning

Alexander Li (UC Berkeley) · Lerrel Pinto (New York University) · Pieter Abbeel (UC Berkeley & covariant.ai)

47、Meta-Gradient Reinforcement Learning with an Objective Discovered Online

Zhongwen Xu (DeepMind) · Hado van Hasselt (DeepMind) · Matteo Hessel (Google DeepMind) · Junhyuk Oh (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

48、TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

Tarun Gogineni (University of Michigan) · Ziping Xu (University of Michigan) · Exequiel Punzalan (University of Michigan) · Runxuan Jiang (University of Michigan) · Joshua Kammeraad (University of Michigan) · Ambuj Tewari (University of Michigan) · Paul Zimmerman (University of Michigan)

49、Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning

Cong Zhang (Nanyang Technological University) · Wen Song (Institute of Marine Scinece and Technology, Shandong University) · Zhiguang Cao (National University of Singapore) · Jie Zhang (Nanyang Technological University) · Puay Siew Tan (SIMTECH) · Xu Chi (Singapore Institute of Manufacturing Technology, A-Star)

50、Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

Qiwen Cui (Peking University) · Lin Yang (UCLA)

51、Instance-based Generalization in Reinforcement Learning

Martin Bertran (Duke University) · Natalia L Martinez (Duke University) · Mariano Phielipp (Intel AI Labs) · Guillermo Sapiro (Duke University)

52、Preference-based Reinforcement Learning with Finite-Time Guarantees

Yichong Xu (Carnegie Mellon University) · Ruosong Wang (Carnegie Mellon University) · Lin Yang (UCLA) · Aarti Singh (CMU) · Artur Dubrawski (Carnegie Mellon University)

53、Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes

Salman Habib (New Jersey Institute of Tech) · Allison Beemer (New Jersey Institute of Technology) · Joerg Kliewer (New Jersey Institute of Technology)

54、BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning

Xinyue Chen (NYU Shanghai) · Zijian Zhou (NYU Shanghai) · Zheng Wang (NYU Shanghai) · Che Wang (New York University) · Yanqiu Wu (New York University) · Keith Ross (NYU Shanghai)

55、Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

Mengdi Xu (Carnegie Mellon University) · Wenhao Ding (Carnegie Mellon University) · Jiacheng Zhu (Carnegie Mellon University) · ZUXIN LIU (Carnegie Mellon University) · Baiming Chen (Tsinghua University) · Ding Zhao (Carnegie Mellon University)

56、On Reward-Free Reinforcement Learning with Linear Function Approximation

Ruosong Wang (Carnegie Mellon University) · Simon Du (Institute for Advanced Study) · Lin Yang (UCLA) · Russ Salakhutdinov (Carnegie Mellon University)

57、Near-Optimal Reinforcement Learning with Self-Play

Yu Bai (Salesforce Research) · Chi Jin (Princeton University) · Tiancheng Yu (MIT )

58、Robust Multi-Agent Reinforcement Learning with Model Uncertainty

Kaiqing Zhang (University of Illinois at Urbana-Champaign (UIUC)) · TAO SUN (Amazon.com) · Yunzhe Tao (Amazon Artificial Intelligence) · Sahika Genc (Amazon Artificial Intelligence) · Sunil Mallya (Amazon AWS) · Tamer Basar (University of Illinois at Urbana-Champaign)

59、Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes

Yi Tian (MIT) · Jian Qian (MIT) · Suvrit Sra (MIT)

60、Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward

Guannan Qu (California Institute of Technology) · Yiheng Lin (California Institute of Technology) · Adam Wierman (California Institute of Technology) · Na Li (Harvard University)

61、Constrained episodic reinforcement learning in concave-convex and knapsack settings

Kianté Brantley (The University of Maryland College Park) · Miro Dudik (Microsoft Research) · Thodoris Lykouris (Microsoft Research NYC) · Sobhan Miryoosefi (Princeton University) · Max Simchowitz (Berkeley) · Aleksandrs Slivkins (Microsoft Research) · Wen Sun (Microsoft Research NYC)

62、Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation

Devavrat Shah (Massachusetts Institute of Technology) · Dogyoon Song (Massachusetts Institute of Technology) · Zhi Xu (MIT) · Yuzhe Yang (MIT)

63、Trajectory-wise Multiple Choice Learning for Dynamics Generalization inReinforcement Learning

Younggyo Seo (KAIST) · Kimin Lee (UC Berkeley) · Ignasi Clavera Gilaberte (UC Berkeley) · Thanard Kurutach (University of California Berkeley) · Jinwoo Shin (KAIST) · Pieter Abbeel (UC Berkeley & covariant.ai)

64、Cooperative Heterogeneous Deep Reinforcement Learning

Han Zheng (UTS) · Pengfei Wei (National University of Singapore) · Jing Jiang (University of Technology Sydney) · Guodong Long (University of Technology Sydney (UTS)) · Qinghua Lu (Data61, CSIRO) · Chengqi Zhang (University of Technology Sydney)

65、Implicit Distributional Reinforcement Learning

Yuguang Yue (University of Texas at Austin) · Zhendong Wang (University of Texas, Austin) · Mingyuan Zhou (University of Texas at Austin)

66、Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sreejith Balakrishnan (National University of Singapore) · Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Harold Soh (National University Singapore)

67、EPOC: A Provably Correct Policy Gradient Approach to Reinforcement Learning

Alekh Agarwal (Microsoft Research) · Mikael Henaff (Microsoft) · Sham Kakade (University of Washington) · Wen Sun (Microsoft Research NYC)

68、Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations

Zhuoran Yang (Princeton) · Chi Jin (Princeton University) · Zhaoran Wang (Northwestern University) · Mengdi Wang (Princeton University) · Michael Jordan (UC Berkeley)

69、Decoupled Policy Gradient Methods for Competitive Reinforcement Learning

Constantinos Daskalakis (MIT) · Dylan Foster (MIT) · Noah Golowich (Massachusetts Institute of Technology)

70、Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss

Shuang Qiu (University of Michigan) · Xiaohan Wei (University of Southern California) · Zhuoran Yang (Princeton) · Jieping Ye (University of Michigan) · Zhaoran Wang (Northwestern University)

71、Improving Generalization in Reinforcement Learning with Mixture Regularization

KAIXIN WANG (National University of Singapore) · Bingyi Kang (National University of Singapore) · Jie Shao (Fudan University) · Jiashi Feng (National University of Singapore)

72、A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning

Arnu Pretorius (InstaDeep) · Scott Cameron (Instadeep) · Elan van Biljon (Stellenbosch University) · Thomas Makkink (InstaDeep) · Shahil Mawjee (InstaDeep) · Jeremy du Plessis (University of Cape Town) · Jonathan Shock (University of Cape Town) · Alexandre Laterre (InstaDeep) · Karim Beguir (InstaDeep)

73、Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

Yunqiu Xu (University of Technology Sydney) · Meng Fang (Tencent) · Ling Chen (" University of Technology, Sydney, Australia") · Yali Du (University College London) · Joey Tianyi Zhou (IHPC, A*STAR) · Chengqi Zhang (University of Technology Sydney)

74、Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Parameswaran Kamalaruban (EPFL) · Yu-Ting Huang (EPFL) · Ya-Ping Hsieh (EPFL) · Paul Rolland (EPFL) · Cheng Shi (Unversity of Basel) · Volkan Cevher (EPFL)

75、Interferobot: aligning an optical interferometer by a reinforcement learning agent

Dmitry Sorokin (Russian Quantum Center) · Alexander Ulanov (Russian Quantum Center) · Ekaterina Sazhina (Russian Quantum Center) · Alexander Lvovsky (Oxford University)

76、Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Yudong Chen (Cornell University) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

77、Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation

Aaron Sonabend (Harvard University) · Junwei Lu () · Leo Anthony Celi (Massachusetts Institute of Technology) · Tianxi Cai (Harvard School of Public Health) · Peter Szolovits (MIT)

78、Dynamic allocation of limited memory resources in reinforcement learning

Nisheet Patel (University of Geneva) · Luigi Acerbi (University of Helsinki) · Alexandre Pouget (University of Geneva)

79、AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

Afshin Oroojlooy (SAS Institute, Inc) · Mohammadreza Nazari (SAS Institute Inc.) · Davood Hajinezhad (SAS Institute Inc.) · Jorge Silva (SAS)

80、Sample-Efficient Reinforcement Learning of Undercomplete POMDPs

Chi Jin (Princeton University) · Sham Kakade (University of Washington) · Akshay Krishnamurthy (Microsoft) · Qinghua Liu (Princeton University)

81、RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

Ziyu Wang (Deepmind) · Caglar Gulcehre (Deepmind) · Alexander Novikov (DeepMind) · Thomas Paine (DeepMind) · Sergio Gómez (DeepMind) · Konrad Zolna (DeepMind) · Rishabh Agarwal (Google Research, Brain Team) · Josh Merel (DeepMind) · Daniel Mankowitz (DeepMind) · Cosmin Paduraru (DeepMind) · Gabriel Dulac-Arnold (Google Research) · Jerry Li (Google) · Mohammad Norouzi (Google Brain) · Matthew Hoffman (DeepMind) · Nicolas Heess (Google DeepMind) · Nando de Freitas (DeepMind)

82、A local temporal difference code for distributional reinforcement learning

Pablo Tano (University of Geneva) · Peter Dayan (Max Planck Institute for Biological Cybernetics) · Alexandre Pouget (University of Geneva)

83、The Value Equivalence Principle for Model-Based Reinforcement Learning

Christopher Grimm (University of Michigan) · Andre Barreto (DeepMind) · Satinder Singh (DeepMind) · David Silver (DeepMind)

84、Steady State Analysis of Episodic Reinforcement Learning

Huang Bojun (Rakuten Institute of Technology)

85、Information-theoretic Task Selection for Meta-Reinforcement Learning

Ricardo Luna Gutierrez (University of Leeds) · Matteo Leonetti (University of Leeds)

86、A Unifying View of Optimism in Episodic Reinforcement Learning

Gergely Neu (Universitat Pompeu Fabra) · Ciara Pike-Burke (Imperial College London)

87、Accelerating Reinforcement Learning through GPU Atari Emulation

Steven Dalton (Nvidia) · iuri frosio (nvidia)

88、Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

Huan Zhang (UCLA) · Hongge Chen (MIT) · Chaowei Xiao (University of Michigan, Ann Arbor) · Bo Li (UIUC) · mingyan liu (university of Michigan, Ann Arbor) · Duane Boning (Massachusetts Institute of Technology) · Cho-Jui Hsieh (UCLA)

89、Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Guangxiang Zhu (Tsinghua university) · Minghao Zhang (Tsinghua University) · Honglak Lee (Google / U. Michigan) · Chongjie Zhang (Tsinghua University)

90、Adaptive Discretization for Model-Based Reinforcement Learning

Sean Sinclair (Cornell University) · Tianyu Wang (Duke University) · Gauri Jain (Cornell University) · Siddhartha Banerjee (Cornell University) · Christina Yu (Cornell University)

91、Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration

Yao Liu (Stanford University) · Adith Swaminathan (Microsoft Research) · Alekh Agarwal (Microsoft Research) · Emma Brunskill (Stanford University)

92、Provably adaptive reinforcement learning in metric spaces

Tongyi Cao (University of Massachusetts Amherst) · Akshay Krishnamurthy (Microsoft)

93、Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

Alex Lee (UC Berkeley) · Anusha Nagabandi (UC Berkeley) · Pieter Abbeel (UC Berkeley & covariant.ai) · Sergey Levine (UC Berkeley)

94、Inverse Reinforcement Learning from a Gradient-based Learner

Giorgia Ramponi (Politecnico di Milano) · Gianluca Drappo (Politecnico di Milano) · Marcello Restelli (Politecnico di Milano)

2、GAN:21 篇

1、BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

Thu Nguyen-Phuoc (University of Bath) · Christian Richardt (University of Bath) · Long Mai (Adobe Research) · Yongliang Yang (University of Bath) · Niloy Mitra (University College London)

2、TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation

Chun-Hsing Lin (National Taiwan University) · Siang-Ruei Wu (National Taiwan University) · Hung-yi Lee (National Taiwan University) · Yun-Nung Chen (National Taiwan University)

3、CircleGAN: Generative Adversarial Learning across Spherical Circles

Woohyeon Shim (Postech) · Minsu Cho (POSTECH)

4、COT-GAN: Generating Sequential Data via Causal Optimal Transport

Tianlin Xu (London School of Economics and Political Science) · Wenliang Le (Gatsby Unit, UCL) · Michael Munn (Google) · Beatrice Acciaio (London School of Economics)

5、HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

6、GramGAN: Deep 3D Texture Synthesis From 2D Exemplars

Tiziano Portenier (ETH Zurich) · Siavash Arjomand Bigdeli (CSEM) · Orcun Goksel (ETH Zurich)

7、ColdGANs: Taming Language GANs with Cautious Sampling Strategies

Thomas Scialom (reciTAL) · Paul-Alexis Dray (reciTAL) · Sylvain Lamprier (LIP6-UPMC) · Benjamin Piwowarski (LIP6, UPMC / CNRS, Paris, France) · Jacopo Staiano (reciTAL)

8、PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

Henry Charlesworth (University of Warwick) · Giovanni Montana (University of Warwick)

Jungil Kong (Kakao Enterprise) · Jaehyeon Kim (Kakao Enterprise) · Jaekyoung Bae (Kakao Enterprise)

9、GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

Dingfan Chen (CISPA - Helmholtz Center for Information Security) · Tribhuvanesh Orekondy (Max Planck Institute for Informatics) · Mario Fritz (CISPA Helmholtz Center i.G.)

10、GANSpace: Discovering Interpretable GAN Controls

Erik Härkönen (Aalto University) · Aaron Hertzmann (Adobe) · Jaakko Lehtinen (Aalto University & NVIDIA) · Sylvain Paris (Adobe)

11、GAN Memory with No Forgetting

Chunyuan Li (Microsoft Research) · Miaoyun Zhao (UNC) · Jianqiao Li (Duke University) · Sijia Wang (Duke University) · Lawrence Carin (Duke University)

12、Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Yue Wu (Carnegie Mellon University) · Pan Zhou (National University of Singapore) · Andrew Gordon Wilson (New York University) · Eric Xing (Petuum Inc. / Carnegie Mellon University) · Zhiting Hu (Carnegie Mellon University)

13、Instance Selection for GANs

Terrance DeVries (University of Guelph) · Michal Drozdzal (FAIR) · Graham W Taylor (University of Guelph)

14、Distributional Robustness with IPMs and links to Regularization and GANs

Hisham Husain (The Australian National University & Data61)

15、Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample

Shir Gur (Tel Aviv University) · Sagie Benaim (Tel Aviv University) · Lior Wolf (Facebook AI Research)

16、Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling

Tong Che (MILA) · Ruixiang ZHANG (Mila/UdeM) · Jascha Sohl-Dickstein (Google Brain) · Hugo Larochelle (Google Brain) · Liam Paull (Université de Montréal) · Yuan Cao (Google Brain) · Yoshua Bengio (Mila / U. Montreal)

17、Teaching a GAN What Not to Learn

Siddarth Asokan (Indian Institute of Science) · Chandra Seelamantula (IISc Bangalore)

18、Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

Samarth Sinha (University of Toronto, Vector Institute) · Zhengli Zhao (UCI, Google Brain) · Anirudh Goyal ALIAS PARTH GOYAL (Université de Montréal) · Colin A Raffel (Google Brain) · Augustus Odena (Google Brain)

19、Differentiable Augmentation for Data-Efficient GAN Training

Shengyu Zhao (Tsinghua University) · Zhijian Liu (MIT) · Ji Lin (MIT) · Jun-Yan Zhu (MIT) · Song Han (MIT)

20、DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs

yaxing wang (Centre de Visió per Computador (CVC)) · Lu Yu (computer vision center, UAB) · Joost van de Weijer (Computer Vision Center Barcelona)

21、Reconstructing Perceptive Images from Brain Activity by Shape-SemanticGAN

Tao Fang (Zhejiang University) · Yu Qi (Zhejiang University) · Gang Pan (Zhejiang University

3、无监督学习(6 篇):

1、Unsupervised Learning of Dense Visual Representations

Pedro O. Pinheiro (Element AI) · Amjad Almahairi (Element AI) · Ryan Benmalek (Cornell University) · Florian Golemo (MILA / ElementAI) · Aaron Courville (U. Montreal)

2、Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

Mathilde Caron (INRIA / FAIR) · Ishan Misra (Facebook AI Research ) · Julien Mairal (Inria) · Priya Goyal (Facebook AI Research) · Piotr Bojanowski (Facebook) · Armand Joulin (Facebook AI research)

3、Unsupervised Learning of Object Landmarks via Self-Training Correspondence

Dimitrios Mallis (Computer Vision Laboratory - University of Nottingham) · Enrique Sanchez (Samsung AI Centre) · Matthew Bell (University of Nottingham) · Georgios Tzimiropoulos (Queen Mary University of London)

4、Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control

Yaofeng Desmond Zhong (Princeton University) · Naomi Leonard (Princeton University)

5、Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Nikolaos Karalias (EPFL) · Andreas Loukas (EPFL)

6、Provably Efficient Exploration for RL with Unsupervised Learning

Fei Feng (University of California, Los Angeles) · Ruosong Wang (Carnegie Mellon University) · Wotao Yin (Alibaba US, DAMO Academy)· Simon Du (Institute for Advanced Study) · Lin Yang (UCLA

4、自监督学习:8 篇

1、Self-supervised learning through the eyes of a child

Emin Orhan (New York University) · Vaibhav Gupta (New York University) · Brenden Lake (New York University)

2、Self-Supervised Learning by Cross-Modal Audio-Video Clustering

Humam Alwassel (KAUST) · Dhruv Mahajan (Facebook) · Bruno Korbar (Facebook) · Lorenzo Torresani (Facebook AI) · Bernard Ghanem (KAUST) · Du Tran (Facebook AI)

3、Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases

Senthil Purushwalkam Shiva Prakash (Carnegie Mellon University) · Abhinav Gupta (Facebook AI Research/CMU)

4、LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration

Bharat Bhatnagar (MPI-INF) · Cristian Sminchisescu (Google Research) · Christian Theobalt (MPI Informatik) · Gerard Pons-Moll (MPII, Germany)

5、Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

Shreyas Fadnavis (Indiana University Bloomington) · Joshua Batson (CZ Biohub) · Eleftherios Garyfallidis (Indiana University)

6、Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning

Jean-Bastien Grill (DeepMind) · Florian Strub (DeepMind) · Florent Altché (DeepMind) · Corentin Tallec (Deepmind) · Pierre Richemond (Imperial College) · Elena Buchatskaya (DeepMind) · Carl Doersch (DeepMind) · Bernardo Avila Pires (DeepMind) · Zhaohan Guo (DeepMind) · Mohammad Gheshlaghi Azar (DeepMind) · Bilal Piot (DeepMind) · koray kavukcuoglu (DeepMind) · Remi Munos (DeepMind) · Michal Valko (DeepMind)

7、CompReSS: Compressing Representations for Self-Supervised Learning

Soroush Abbasi Koohpayegani (University of Maryland Baltimore County) · Ajinkya Tejankar (University of Maryland Baltimore County) · Hamed Pirsiavash (University of Maryland, Baltimore County)

8、wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Alexei Baevski (Facebook AI Research) · Yuhao Zhou (University of Toronto) · Abdel-rahman Mohamed (Facebook AI Research (FAIR)) · Michael Auli (Facebook AI Research

5、半监督学习:14 篇

1、Semi-Supervised Neural Architecture Search

Renqian Luo (University of Science and Technology of China) · Xu Tan (Microsoft Research) · Rui Wang (Microsoft Research Asia) · Tao Qin (Microsoft Research) · Enhong Chen

2、Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization

Wei Wang (Southeast University) · Min-Ling Zhang (Southeast University)

3、Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning

Qi Wang (Northwestern Polytechnical University) · Tao Han (Northwestern Polytechnical University) · Junyu Gao (Northwestern Polytechnical University, Center for OPTical IMagery Analysis and Learning) · Yuan Yuan (Northwestern Polytechnical University)

4、FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Kihyuk Sohn (NEC Laboratories America) · David Berthelot (Google Brain) · Nicholas Carlini (Google) · Zizhao Zhang (Google) · Han Zhang (Google) · Colin A Raffel (Google Brain) · Ekin Dogus Cubuk (Google Brain) · Alexey Kurakin (Google Brain) · Chun-Liang Li (Google)

5、Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning

Zhongzheng Ren (UIUC) · Raymond Yeh (University of Illinois at Urbana–Champaign) · Alexander Schwing (University of Illinois at Urbana-Champaign)

6、VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain

Jinsung Yoon (University of California, Los Angeles) · Yao Zhang (University of Cambridge) · James Jordon (University of Oxford) · Mihaela van der Schaar (University of Cambridge)

7、Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

Yuxi Li (Shanghai Jiao Tong University) · Jinlong Peng (Tencent Youtu Lab) · Ning Xu (Adobe Research) · John See (Multimedia University) · Weiyao Lin (Shanghai Jiao Tong university)

(University of Science and Technology of China) · Tie-Yan Liu (Microsoft Research Asia)

8、Graph Stochastic Neural Networks for Semi-supervised Learning

Haibo Wang (Tsinghua University) · Chuan Zhou (Chinese Academy of Sciences) · Xin Chen (Institute for Network Sciences and Cyberspace, Tsinghua University) · Jia Wu (Macquarie University) · Shirui Pan (Monash University) · Jilong Wang (Tsinghua University)

9、Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Wenzheng Feng (Tsinghua University) · Jie Zhang (Webank Co.,Ltd) · Yuxiao Dong (Microsoft) · Yu Han (Tsinghua University) · Huanbo Luan (Tsinghua University) · Qian Xu (WeBank) · Qiang Yang (WeBank and HKUST) · Evgeny Kharlamov (Bosch Center for Artificial Intelligence) · Jie Tang (Tsinghua University)

10、Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

Meng Liu (Purdue University) · David Gleich (Purdue University)

11、Uncertainty Aware Semi-Supervised Learning on Graph Data

Xujiang Zhao (The University of Texas at Dallas) · Feng Chen (UT Dallas) · Shu Hu (University at Buffalo, State University of New York) · Jin-Hee Cho (Virginia Tech)

12、Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

Jaehyung Kim (KAIST) · Youngbum Hur (Samsung Advanced Institute of Technology) · Sejun Park (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS) · Jinwoo Shin (KAIST)

13、Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Anqi Wu () · E. Kelly Buchanan (Columbia University) · Matthew Whiteway (Columbia University) · Michael Schartner (University of Geneva) · Guido Meijer (Champalimaud Center for the Unknown) · Jean-Paul Noel (New York University) · Erica Rodriguez (Columbia University) · Claire Everett (Columbia University) · Amy Norovich (Columbia University) · Evan Schaffer (Columbia University) · Neeli Mishra (Columbia University) · C. Daniel Salzman (Columbia University) · Dora Angelaki (New York University) · Andrés Bendesky (Columbia University) · The International Brain Laboratory The International Brain Laboratory (The International Brain Laboratory) · John Cunningham (University of Columbia) · Liam Paninski (Columbia University)

14、The Unreasonable Effectiveness of Big Models for Semi-Supervised Learning

Ting Chen (Google) · Simon Kornblith (Google Brain) · Kevin Swersky (Google) · Mohammad Norouzi (Google Brain) · Geoffrey E Hinton (Google & University of Toronto)


6、迁移学习:9 篇

1、Transfer Learning via ℓ1 Regularization

Masaaki Takada (Toshiba Corporation) · Hironori Fujisawa (The Institute of Statistical Mathematics)

2、Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning

Han Cai (Massachusetts Institute of Technology) · Chuang Gan (MIT-IBM Watson AI Lab) · Ligeng Zhu (MIT) · Song Han (MIT)

3、Co-Tuning for Transfer Learning

Kaichao You (Tsinghua University) · Zhi Kou (Tsinghua University) · Mingsheng Long (Tsinghua University) · Jianmin Wang (Tsinghua University)

4、Shared Space Transfer Learning for analyzing multi-site fMRI data

Muhammad Yousefnezhad (University of Alberta) · Alessandro Selvitella (Purdue University Fort Wayne) · Daoqiang Zhang (Nanjing University of Aeronautics and Astronautics) · Andrew Greenshaw (University of Alberta) · Russell Greiner (University of Alberta)

5、On the Theory of Transfer Learning: The Importance of Task Diversity

Nilesh Tripuraneni (UC Berkeley) · Michael Jordan (UC Berkeley) · Chi Jin (Princeton University)

6、Hierarchical Granularity Transfer Learning

Shaobo Min (USTC) · Hongtao Xie (University of Science and Technology of China) · Hantao Yao ( Institute of Automation, Chinese Academy of Sciences) · Xuran Deng (University of Science and Technology of China) · Zheng-Jun Zha (University of Science and Technology of China) · Yongdong Zhang (University of Science and Technology of China)

7、What is being transferred in transfer learning?

Behnam Neyshabur (Google) · Hanie Sedghi (Google Brain) · Chiyuan Zhang (Google Brain)

8、Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks

Mir Mohammadreza Mousavi Kalan (University of Southern California) · Zalan Fabian (University of Southern California) · Salman Avestimehr (University of Southern California) · Mahdi Soltanolkotabi (University of Southern california)

9、A Combinatorial Perspective on Transfer Learning

Jianan Wang (DeepMind) · Eren Sezener (DeepMind) · David Budden (DeepMind) · Marcus Hutter (DeepMind) · Joel Veness (Deepmind)

7、主动学习:4 篇

1、Exemplar Guided Active Learning

Jason Hartford (University of British Columbia) · Kevin Leyton-Brown (University of British Columbia) · Hadas Raviv (AI21 Labs) · Dan Padnos (AI21 Labs) · Shahar Lev (AI21 Labs) · Barak Lenz (AI21 Labs)

2、Finding the Homology of Decision Boundaries with Active Learning

Weizhi Li (Arizona State University) · Gautam Dasarathy (Arizona State University) · Karthikeyan Natesan Ramamurthy (IBM Research) · Visar Berisha (Arizona State University)

3、Efficient active learning of sparse halfspaces with arbitrary bounded noise

Chicheng Zhang (University of Arizona) · Jie Shen (Stevens Institute of Technology) · Pranjal Awasthi (Rutgers University/Google)

4、Graph Policy Network for Transferable Active Learning on Graphs

Shengding Hu (Tsinghua University) · Zheng Xiong (Tsinghua University / University of Oxford) · Meng Qu (Mila) · Xingdi Yuan (Microsoft Research) · Marc-Alexandre Côté (Microsoft Research) · Zhiyuan Liu (Tsinghua University) · Jian Tang (Mila)

8、元学习:23 篇

1、Meta-Learning through Hebbian Plasticity in Random Networks

Elias Najarro (IT University of Copenhagen) · Sebastian Risi (IT University of Copenhagen)

2、Meta-learning from Tasks with Heterogeneous Attribute Spaces

Tomoharu Iwata (NTT) · Atsutoshi Kumagai (NTT Software Innovation Center)

3、Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

4、Meta-Learning Requires Meta-Augmentation

Janarthanan Rajendran (University of Michigan) · Alexander Irpan (Google Brain) · Eric Jang (Google Brain)

5、A meta-learning approach to (re)discover plasticity rules that carve a desired function to a neural network

Basile Confavreux (University of Oxford) · Friedemann Zenke (Friedrich Miescher Institute) · Everton Agnes (University of Oxford) · Timothy Lillicrap (DeepMind & UCL) · Tim Vogels (Institute of Science and Technology)

6、Robust Meta-learning for Mixed Linear Regression with Small Batches

Weihao Kong (Stanford University) · Raghav Somani (University of Washington) · Sham Kakade (University of Washington) · Sewoong Oh (University of Washington)

7、Modular Meta-Learning with Shrinkage

Yutian Chen (DeepMind) · Abram Friesen (DeepMind) · Feryal Behbahani (DeepMind) · Arnaud Doucet (Google DeepMind) · David Budden (DeepMind) · Matthew Hoffman (DeepMind) · Nando de Freitas (DeepMind)

8、Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Massimiliano Patacchiola (University of Edinburgh) · Jack Turner (University of Edinburgh) · Elliot J. Crowley (University of Edinburgh) · Michael O'Boyle (University of Edinburgh) · Amos Storkey (University of Edinburgh)

9、MetaSDF: Meta-Learning Signed Distance Functions

Vincent Sitzmann (Stanford University) · Eric Chan (Stanford University) · Richard Tucker (Google) · Noah Snavely (Cornell University and Google AI) · Gordon Wetzstein (Stanford University)

10、OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

Taewon Jeong (KAIST) · Heeyoung Kim (KAIST)

11、Submodular Meta-Learning

Arman Adibi (University of Pennsylvania) · Aryan Mokhtari (UT Austin) · Hamed Hassani (UPenn)

12、Continuous Meta-Learning without Tasks

James Harrison (Stanford University) · Apoorva Sharma (Stanford University) · Chelsea Finn (Stanford) · Marco Pavone (Stanford University)

Andrew Foong (University of Cambridge) · Wessel Bruinsma (Invenia Labs and University of Cambridge) · Jonathan Gordon (University of Cambridge) · Yann Dubois (Facebook AI Research) · James Requeima (University of Cambridge / Invenia Labs) · Richard E Turner (University of Cambridge)

13、Online Structured Meta-learning

Huaxiu Yao (Pennsylvania State University) · Yingbo Zhou (Salesforce Research) · Mehrdad Mahdavi (Pennsylvania State University) · Zhenhui (Jessie) Li (Penn State University) · Richard Socher (Salesforce) · Caiming Xiong (Salesforce)

14、Probabilistic Active Meta-Learning

Jean Kaddour (Imperial College London) · Steindor Saemundsson (Imperial College London) · Marc Deisenroth (University College London)

15、Gradient-EM Bayesian Meta-Learning

Yayi Zou (Didi Research America) · Xiaoqi Lu (Columbia University)

16、Task-Robust Model-Agnostic Meta-Learning

Liam Collins (University of Texas at Austin) · Aryan Mokhtari (UT Austin) · Sanjay Shakkottai (University of Texas at Austin)

17、Structured Prediction for Conditional Meta-Learning

Ruohan Wang (Imperial College London) · Yiannis Demiris (Imperial College London) · Carlo Ciliberto (Imperial College London)

18、Modeling and Optimization Trade-off in Meta-learning

Katelyn Gao (Intel Labs) · Ozan Sener (Intel Labs)

19、Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Micah Goldblum (University of Maryland) · Liam Fowl (University of Maryland) · Tom Goldstein (University of Maryland)

20、A Closer Look at the Training Strategy for Modern Meta-Learning

JIAXIN CHEN (The Hong Kong Polytechnic University) · Xiao-Ming Wu (The Hong Kong Polytechnic University) · Yanke Li (ETH Zurich) · Qimai LI (The Hong Kong PolyU) · Li-Ming Zhan (The Hong Kong Polytechnic University) · Fu-lai Chung (The Hong Kong Polytechnic University)

21、Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters

Kaiyi Ji (The Ohio State University) · Jason Lee (Princeton University) · Yingbin Liang (The Ohio State University) · H. Vincent Poor (Princeton University)

22、The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning

Giulia Denevi (IIT & UNIGE) · Massimiliano Pontil (IIT & UCL) · Carlo Ciliberto (Imperial College London)

23、Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

Alireza Fallah (MIT) · Aryan Mokhtari (UT Austin) · Asuman Ozdaglar (Massachusetts Institute of Technology

9、联邦学习:9 篇

1、Robust Federated Learning: The Case of Affine Distribution Shifts

Amirhossein Reisizadeh (UC Santa Barbara) · Farzan Farnia (Stanford University) · Ramtin Pedarsani (UC Santa Barbara) · Ali Jadbabaie (MIT)

2、Personalized Federated Learning with Moreau Envelopes

Canh T. Dinh (The University of Sydney) · Nguyen H. Tran (The University of Sydney) · Tuan Dung Nguyen (The University of Melbourne)

3、Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach

Alireza Fallah (MIT) · Aryan Mokhtari (UT Austin) · Asuman Ozdaglar (Massachusetts Institute of Technology)

4、An Efficient Framework for Clustered Federated Learning

Avishek Ghosh (University of California, Berkeley) · Jichan Chung (University of California, Berkeley) · Dong Yin (DeepMind) · Kannan Ramchandran (UC Berkeley)

5、Optimal Topology Design for Cross-Silo Federated Learning

Othmane MARFOQ (Inria / Accenture) · CHUAN XU (Inria Sophia Antipolis) ·

Giovanni Neglia (Inria) · Richard Vidal (Accenture)

6、Ensemble Distillation for Robust Model Fusion in Federated Learning

Tao Lin (EPFL) · Lingjing Kong (EPFL) · Sebastian U Stich (EPFL) ·

Martin Jaggi (EPFL)

7、Attack of the Tails: Yes, You Really Can Backdoor Federated Learning

Hongyi Wang (University of Wisconsin-Madison) · Kartik Sreenivasan (University of Wisconsin-Madison) · Shashank Rajput (University of Wisconsin - Madison) · Harit Vishwakarma (University of Wisconsin Madison) · Jy-yong Sohn (KAIST) · Saurabh Agarwal (UW-Madison) · Kangwook Lee (UW Madison) · Dimitris Papailiopoulos (University of Wisconsin-Madison)

8、Inverting Gradients - How easy is it to break privacy in federated learning?

Jonas Geiping (University of Siegen) · Hartmut Bauermeister (University of Siegen) · Hannah Dröge (University of Siegen) · Michael Moeller (University of Siegen)

9、Lower Bounds and Optimal Algorithms for Personalized Federated Learning

Filip Hanzely (KAUST) · Slavomír Hanzely (KAUST) · Samuel Horváth (King Abdullah University of Science and Technology) · Peter Richtarik (KAUST)

10、多模态:7 篇

1、Multimodal Graph Networks for Compositional Generalization in Visual Question Answering

Raeid Saqur (Princeton University) · Karthik Narasimhan (Princeton University)

2、Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence

Thomas M Sutter (ETH Zurich) · Imant Daunhawer (ETH Zurich) · Julia Vogt (ETH Zurich)

3、Deep Multimodal Fusion by Channel Exchanging

Yikai Wang (Tsinghua University) · Wenbing Huang (Tsinghua University) · Fuchun Sun (Tsinghua) · Tingyang Xu (Tencent AI Lab) · Yu Rong (Tencent AI Lab) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

4、Self-Supervised MultiModal Versatile Networks

Jean-Baptiste Alayrac (Deepmind) · Adria Recasens (DeepMind) · Rosalia Schneider (DeepMind) · Relja Arandjelović (DeepMind) · Jason Ramapuram (University of Geneva) · Jeffrey De Fauw (DeepMind) · Lucas Smaira (DeepMind) · Sander Dieleman (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)

5、CoMIR: Contrastive Multimodal Image Representation for Registration

Nicolas Pielawski (Uppsala University) · Elisabeth Wetzer (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Johan Öfverstedt (Department of Information Technology, Uppsala University) · Jiahao Lu (Uppsala University) · Carolina Wählby (Uppsala University) · Joakim Lindblad (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden) · Natasa Sladoje (Centre for Image Analysis, Department of Information Technology, Uppsala University, Sweden)

6、Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Masha Itkina (Stanford University) · Boris Ivanovic (Stanford University) · Ransalu Senanayake (Stanford University) · Mykel J Kochenderfer (Stanford University) · Marco Pavone (Stanford University)

7、The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

Douwe Kiela (Facebook AI Research) · Hamed Firooz (Facebook) · Aravind Mohan (Facebook) · Vedanuj Goswami (Facebook) · Amanpreet Singh (Facebook) · Pratik Ringshia (Facebook) · Davide Testuggine (Facebook

5

One/Few/Zero-shot、OOD

One-shot:5 篇

1、Make One-Shot Video Object Segmentation Efficient Again

Tim Meinhardt (TUM) · Laura Leal-Taixé (TUM)

2、Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

Yawei Luo (Zhejiang University) · Ping Liu (UTS) · Tao Guan (Huazhong University of Science and Technology) · Junqing Yu (Huazhong University of Science & Technology) · Yi Yang (UTS)

3、Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

Han Shi (Hong Kong University of Science and Technology) · Renjie Pi (Huawei Noah’s Ark Lab) · Hang Xu (Huawei Noah's Ark Lab) · Zhenguo Li (Noah's Ark Lab, Huawei Tech Investment Co Ltd) · James Kwok (Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

4、Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Shali Jiang (Washington University in St. Louis) · Daniel Jiang (Facebook) · Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Jacob Gardner (University of Pennsylvania) · Roman Garnett (Washington University in St. Louis)

5、Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

Houwen Peng (Microsoft Research) · Hao Du (Microsoft Research) · Hongyuan Yu (MSRA) · QI LI (Tsinghua Univeristy) · Jing Liao (City University of Hong Kong) · Jianlong Fu (Microsoft Research

Few-shot:14 篇

1Few-shot Image Generation via Self-Adaptation

Yijun Li (Adobe Research) · Richard Zhang (Adobe) · Jingwan (Cynthia) Lu (Adobe Research) · Eli Shechtman (Adobe Research, US)

2Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning

Youngsung Kim (Samsung Advanced Institute of Technology) · Jinwoo Shin (KAIST) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics) · Sung Ju Hwang (KAIST, AITRICS)

3、Interventional Few-Shot Learning

Zhongqi Yue (Nanyang Technological University) · Hanwang Zhang (NTU) · Qianru Sun (Singapore Management University) · Xian-Sheng Hua (Damo Academy, Alibaba Group)

4、Self-Supervised Few-Shot Learning on Point Clouds

Charu Sharma (Indian Institute of Technology Hyderabad) · Manohar Kaul (IITH)

5、Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Micah Goldblum (University of Maryland) · Liam Fowl (University of Maryland) · Tom Goldstein (University of Maryland)

6、Information Maximization for Few-Shot Learning

Malik Boudiaf (Ecole de Technologie Superieure) · Imtiaz Ziko (Ecole de technologie superieure (ETS)) · Jérôme Rony (ÉTS Montréal) · Jose Dolz (ETS Montreal) · Pablo Piantanida (CentraleSupélec - Mila) · Ismail Ben Ayed (ETS Montreal)

7、Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Massimiliano Patacchiola (University of Edinburgh) · Jack Turner (University of Edinburgh) · Elliot J. Crowley (University of Edinburgh) · Michael O'Boyle (University of Edinburgh) · Amos Storkey (University of Edinburgh)

8、OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

9、One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

Saurabh Kumar (Stanford University) · Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford)

10、Restoring Negative Information in Few-Shot Object Detection

Yukuan Yang (Tsinghua University) · Fangyun Wei (Microsoft Research Asia) · Miaojing Shi (King's College London) · Guoqi Li (Tsinghua University)11、Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingLin Lan (Xi'an Jiaotong University) · Pinghui Wang (Xi'an Jiaotong University) · Xuefeng Du (Xi'an Jiaotong University) · Kaikai Song (Huawei Noah's Ark Lab) · Jing Tao (Xi'an Jiaotong University) · Xiaohong Guan (Xi'an Jiaotong University)12、CrossTransformers: spatially-aware few-shot transferCarl Doersch (DeepMind) · Ankush Gupta (DeepMind) · Andrew Zisserman (DeepMind & University of Oxford)13、Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link PredictionJinheon Baek (KAIST) · Dong Bok Lee (KAIST) · Sung Ju Hwang (KAIST, AITRICS)14、Language Models are Few-Shot LearnersTom B Brown (Google Brain) · Benjamin Mann (OpenAI) · Nick Ryder (OpenAI) · Melanie Subbiah (OpenAI) · Jared D Kaplan (Johns Hopkins University) · Prafulla Dhariwal (OpenAI) · Arvind Neelakantan (OpenAI) · Pranav Shyam (OpenAI) · Girish Sastry (OpenAI) · Amanda Askell (OpenAI) · Sandhini Agarwal (OpenAI) · Ariel Herbert-Voss (OpenAI) · Gretchen M Krueger (OpenAI) · Tom Henighan (OpenAI) · Rewon Child (OpenAI) · Aditya Ramesh (OpenAI) · Daniel Ziegler (OpenAI) · Jeffrey Wu (OpenAI) · Clemens Winter (OpenAI) · Chris Hesse (OpenAI) · Mark Chen (OpenAI) · Eric Sigler (OpenAI) · Mateusz Litwin (OpenAI) · Scott Gray (OpenAI) · Benjamin Chess (OpenAI) · Jack Clark (OpenAI) · Christopher Berner (OpenAI) · Sam McCandlish (OpenAI) · Alec Radford (OpenAI) · Ilya Sutskever (OpenAI) · Dario Amodei (OpenAI)Zero-shot:5 篇

1、Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Michael Dennis (University of California Berkeley) · Natasha Jaques (MIT) · Eugene Vinitsky (UC Berkeley) · Alexandre Bayen (UC Berkeley)· Stuart Russell (UC Berkeley) · Andrew Critch (UC Berkeley) · Sergey Levine (UC Berkeley)

2、Dense Feature Composition for Zero-Shot Learning

Dat Huynh (Northeastern University) · Ehsan Elhamifar (Northeastern University)

3、Attribute Prototype Network for Zero-Shot Learning

Wenjia Xu (University of Chinese Academy of Sciences) · Yongqin Xian (Max Planck Institute Informatics) · Jiuniu Wang (City University of Hong Kong) · Bernt Schiele (Max Planck Institute for Informatics) · Zeynep Akata (University of Tübingen)

4、Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation

Ping Hu (Boston University) · Stan Sclaroff (Boston University) · Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

5、Consistent Structural Relation Learning for Zero-Shot Segmentation

Peike Li (University of Technology Sydney) · Yunchao Wei (UTS) · Yi Yang (UTS)

6、Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

Mariya Toneva (Carnegie Mellon University) · Otilia Stretcu (Carnegie Mellon University) · Barnabas Poczos (Carnegie Mellon University) · Leila Wehbe (Carnegie Mellon University) · Tom Mitchell (Carnegie Mellon University)

Out-of-Distribution 7 篇其中 Lecun 转发了第一篇论文。6


网络、自编码器图神经网络(GNN):28 篇

 1、Subgraph Neural Networks

Emily Alsentzer (MIT) · Samuel Finlayson (Harvard Medical School) · Michelle Li (Harvard Medical School) · Marinka Zitnik (Harvard University)

2、Can Graph Neural Networks Count Substructures?

Zhengdao Chen (New York University) · Lei Chen (New York University) · Soledad Villar (New York University) · Joan Bruna (NYU)

3、Factor Graph Neural Networks

Zhen Zhang (University of Adelaide) · Fan Wu (Nanjing University) · Wee Sun Lee (National University of Singapore)

4、Implicit Graph Neural Networks

Fangda Gu (UC Berkeley) · Heng Chang (Tsinghua University) · Wenwu Zhu (Tsinghua University) · Somayeh Sojoudi (University of California, Berkeley) · Laurent El Ghaoui (UC Berkeley)

5、Reliable Graph Neural Networks via Robust Location Estimation

Simon Geisler (Technical University of Munich) · Daniel Zügner (Technical University of Munich) · Stephan Günnemann (Technical University of Munich)

6、Attribution for Graph Neural Networks

Benjamin Sanchez-Lengeling (Google Research) · Jennifer Wei (Google Research) · Brian Lee (Google Inc.) · Emily Reif (Google) · Peter Wang (Columbia University) · Wesley Wei Qian (University of Illinois at Urbana-Champaign) · Kevin McCloskey (Google) · Lucy Colwell (Google) · Alexander Wiltschko (Google Brain)

7、Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Defu Cao (Peking University) · Yujing Wang (MSRA) · Juanyong Duan (Microsoft) · Ce Zhang (ETH Zurich) · Xia Zhu (Microsoft) · Congrui Huang (Microsoft) · Yunhai Tong (Peking University) · Bixiong Xu (Microsoft) · Jing Bai (Microsoft) · Jie Tong (Microsoft) · Qi Zhang (Microsoft)

8、GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

Xiang Zhang (Harvard University) · Marinka Zitnik (Harvard University)

9、Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs

Hao Tang (Shanghai Jiao Tong University) · Zhiao Huang (University of California San Diego) · Jiayuan Gu (University of California, San Diego) · Bao-Liang Lu (Shanghai Jiao Tong University) · Hao Su (UCSD)

10、Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning

Pan Li (Stanford University - Purdue University) · Yanbang Wang (Stanford University) · Hongwei Wang (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

11、Rethinking pooling in graph neural networks

Diego Mesquita (Aalto University) · Amauri Souza (IFCE) · Samuel Kaski (Aalto University and University of Manchester)

12、Design Space for Graph Neural Networks

Jiaxuan You (Stanford University) · Zhitao Ying (Stanford University) · Jure Leskovec (Stanford University and Pinterest)

13、Bandit Samplers for Training Graph Neural Networks

Ziqi Liu (Ant Financial) · Zhengwei Wu (Ant Financial) · Zhiqiang Zhang (Ant Financial Services Group) · Jun Zhou (Ant Financial) · Shuang Yang (Ant Financial) · Le Song (Ant Financial Services Group) · Yuan Qi (Ant Financial Services Group)

14、Pre-Training Graph Neural Networks: A Contrastive Learning Framework with Augmentations

Yuning You (Texas A&M University) · Tianlong Chen (Unversity of Texas at Austin) · Yongduo Sui (University of Science and Technology of China) · Ting Chen (Google) · Zhangyang Wang (University of Texas at Austin) · Yang Shen (Texas A&M University)

15、Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

Jiong Zhu (University of Michigan) · Yujun Yan (University of Michigan) · Lingxiao Zhao (Carnegie Mellon University) · Mark Heimann (University of Michigan) · Leman Akoglu (CMU) · Danai Koutra (U Michigan)

16、Scalable Graph Neural Networks via Bidirectional Propagation

Ming Chen (Renmin University of China) · Zhewei Wei (Renmin University of China) · Bolin Ding ("Data Analytics and Intelligence Lab, Alibaba Group") · Yaliang Li (Alibaba Group) · Ye Yuan ( Beijing Institute of Technology) · Xiaoyong Du (Renmin University of China) · Ji-Rong Wen (Renmin University of China)

17、Towards Deeper Graph Neural Networks with Differentiable Group Normalization

Kaixiong Zhou (Texas A&M University) · Xiao Huang (The Hong Kong Polytechnic University) · Yuening Li (Texas A&M University) · Daochen Zha (Texas A&M University) · Rui Chen (Samsung Research America) · Xia Hu (Texas A&M University)

18、Strongly Incremental Constituency Parsing with Graph Neural Networks

Kaiyu Yang (Princeton University) · Jia Deng (Princeton University)

19、Graphon Neural Networks and the Transferability of Graph Neural Networks

Luana Ruiz (University of Pennsylvania) · Luiz Chamon (University of Pennsylvania) · Alejandro Ribeiro (University of Pennsylvania)

20、Adversarial Attack on Graph Neural Networks with Limited Node Access

Jiaqi Ma (University of Michigan) · Shuangrui Ding (University of Michigan) · Qiaozhu Mei (University of Michigan)

21、Path Integral Based Convolution and Pooling for Graph Neural Networks

Zheng Ma (Princeton University) · Junyu Xuan (University of Technology Sydney) · Yu Guang Wang (University of New South Wales; MPI MiS) · Ming Li (Zhejiang Normal University) · Pietro Liò (University of Cambridge)

22、How hard is to distinguish graphs with graph neural networks?

Andreas Loukas (EPFL)

23、Parameterized Explainer for Graph Neural Network

Dongsheng Luo (The Pennsylvania State University) · Wei Cheng (NEC Labs America) · Dongkuan Xu (The Pennsylvania State University) · Wenchao Yu (UCLA) · Bo Zong (NEC Labs) · Haifeng Chen (NEC Labs America) · Xiang Zhang (The Pennsylvania State University)

24、Building powerful and equivariant graph neural networks with message-passing

Clément Vignac (EPFL) · Andreas Loukas (EPFL) · Pascal Frossard (EPFL)

25、Random Walk Graph Neural Networks

Giannis Nikolentzos (Athens University of Economics and Business) · Michalis Vazirgiannis (École Polytechnique)

26、Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks

David Bieber (Google Brain) · Charles Sutton (Google) · Hugo Larochelle (Google Brain) · Daniel Tarlow (Google Brain)

27、PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Minh N Vu (University of Florida) · My T. Thai (University of Florida)

28、Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks

Kenta Oono (The University of Tokyo, Preferred Networks Inc.) · Taiji Suzuki (The University of Tokyo/RIKEN-AIP)

图卷积网络:2 篇

1、Cross-scale Internal Graph Convolution Network for Image Super-Resolution

Shangchen Zhou (Nanyang Technological University) · Jiawei Zhang (Sensetime Research) · Wangmeng Zuo (Harbin Institute of Technology) · Chen Change Loy (Nanyang Technological University)


2、Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks


Hongwei Jin (University of Illinois at Chicago) · Zhan Shi (University of Illinois at Chicago) · Venkata Jaya Shankar Ashish Peruri (University of Illinois at Chicago) · Xinhua Zhang (UIC)



胶囊网络:1 篇


自编码器 Autoencoder:11 篇


1、Autoencoders that don't overfit towards the Identity

Harald Steck (Netflix)

2、Swapping Autoencoder for Deep Image Manipulation

Taesung Park (UC Berkeley) · Jun-Yan Zhu (Adobe, CMU) · Oliver Wang (Adobe Research) · Jingwan Lu (Adobe Research) · Eli Shechtman (Adobe Research, US) · Alexei Efros (UC Berkeley) · Richard Zhang (Adobe)

3、Hierarchical Quantized Autoencoders

Will Williams (Speechmatics) · Sam Ringer (Speechmatics) · Tom Ash (Speechmatics) · David MacLeod (Speechmatics) · Jamie Dougherty (Speechmatics) · John Hughes (Speechmatics)

4、Implicit Rank-Minimizing Autoencoder

Li Jing (Facebook AI Research) · Jure Zbontar (Facebook) · yann lecun (Facebook)

5、Dirichlet Graph Variational Autoencoder

Jia Li (The Chinese University of Hong Kong) · Jianwei Yu (CUHK) · Jiajin Li (The Chinese University of Hong Kong) · Honglei Zhang (Georgia Institute of Technology) · Kangfei Zhao (The Chinese University of Hong Kong) · Yu Rong (Tencent AI Lab) · Hong Cheng (The Chinese University of Hong Kong) · Junzhou Huang (University of Texas at Arlington / Tencent AI Lab)

6、Regularized linear autoencoders recover the principal components, eventually

Xuchan Bao (University of Toronto) · James Lucas (University of Toronto) · Sushant Sachdeva (University of Toronto) · Roger Grosse (University of Toronto)

7、Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Yi Zhou (University of Southern California) · Chenglei Wu (Facebook) · Zimo Li (University of Southern California) · Chen Cao (Snap Inc.) · Yuting Ye (Facebook Reality Labs) · Jason Saragih (Facebook) · Hao Li (Pinscreen/University of Southern California/USC ICT) · Yaser Sheikh (Facebook Reality Labs)

8、The Autoencoding Variational Autoencoder

Taylan Cemgil (DeepMind) · Sumedh Ghaisas (DeepMind) · Krishnamurthy Dvijotham (DeepMind) · Sven Gowal (DeepMind) · Pushmeet Kohli (DeepMind)

9、Recursive Inference for Variational Autoencoders

Minyoung Kim (Samsung AI Center) · Vladimir Pavlovic (Rutgers University)

10、NVAE: A Deep Hierarchical Variational Autoencoder

Arash Vahdat (NVIDIA) · Jan Kautz (NVIDIA)

11、Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

Masha Itkina (Stanford University) · Boris Ivanovic (Stanford University) · Ransalu Senanayake (Stanford University) · Mykel J Kochenderfer (Stanford University) · Marco Pavone (Stanford University)

7


量化、剪枝、压缩、NAS

量化:7 篇


1、Adaptive Gradient Quantization for Data-Parallel SGD

Fartash Faghri (University of Toronto) · Iman Tabrizian (University of Toronto) · Ilia Markov (IST Austria) · Dan Alistarh (IST Austria & Neural Magic Inc.) · Daniel Roy (Univ of Toronto & Vector) · Ali Ramezani-Kebrya (Vector Institute)

2、Robust Quantization: One Model to Rule Them All

Moran Shkolnik (Intel) · Brian Chmiel (Intel) · Ron Banner (Intel - Artificial Intelligence Products Group (AIPG)) · Gil Shomron (Technion - Israel Institute of Technology) · Yury Nahshan (Intel - Artificial Intelligence Products Group (AIPG)) · Alex Bronstein (Technion) · Uri Weiser (Technion - Israel Institute of Technology)

3、Position-based Scaled Gradient for Model Quantization and Sparse Training

Jangho Kim (Seoul National University) · KiYoon Yoo (Seoul National University) · Nojun Kwak (Seoul National University)

4、AMQ: Automatic Mixed-precision Quantization Based on Hessian Trace

Zhen Dong (UC Berkeley) · Zhewei Yao (UC Berkeley) · Daiyaan Arfeen (UC Berkeley) · Amir Gholami (University of California, Berkeley) · Michael Mahoney (UC Berkeley) · Kurt Keutzer (EECS, UC Berkeley)

5、FleXOR: Trainable Fractional Quantization

Dongsoo Lee (Samsung Research) · Se Jung Kwon (Samsung Research) · Byeongwook Kim (Samsung Research) · Yongkweon Jeon (Samsung Research) · Baeseong Park (samsung research) · Jeongin Yun (Samsung Research)

6、Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow

Didrik Nielsen (DTU Compute) · Ole Winther (DTU and KU)

7、Bayesian Bits: Unifying Quantization and Pruning

Mart van Baalen (Qualcomm) · Christos Louizos (Qualcomm AI Research) · Markus Nagel (Qualcomm) · Rana Ali Amjad (Qualcomm) · Ying Wang (Qualcomm) · Tijmen Blankevoort (Qualcomm) · Max Welling (University of Amsterdam / Qualcomm AI Research)

剪枝:14 篇


1、Pruning Filter in Filter

Fanxu Meng (Harbin Institute of Technology, Shenzhen) · Hao Cheng (Tencent) · Ke Li (Tencent) · Huixiang Luo (Tencent) · Xiaowei Guo (Tencent Youtu Lab) · Guangming Lu (Harbin Institute of Technology, Shenzhen) · Xing Sun (Tencent)

2、Pruning neural networks without any data by conserving synaptic flow

Hidenori Tanaka (NTT Research, PHI Lab / Stanford University) · Daniel Kunin (Stanford University) · Daniel Yamins (Stanford University) · Surya Ganguli (Stanford)

3、Network Pruning via Greedy Optimization: Fast Rate and Efficient Algorithms

Mao Ye (The University of Texas at Austin) · Lemeng Wu (UT Austin) · Qiang Liu (UT Austin)

4、HYDRA: Pruning Adversarially Robust Neural Networks

Vikash Sehwag (Princeton University) · Shiqi Wang (Columbia) · Prateek Mittal (Princeton University) · Suman Jana (Columbia University)

5、Logarithmic Pruning is All You Need

Laurent Orseau (DeepMind) · Marcus Hutter (DeepMind) · Omar Rivasplata (DeepMind & UCL)

6、Bayesian Bits: Unifying Quantization and Pruning

Mart van Baalen (Qualcomm) · Christos Louizos (Qualcomm AI Research) · Markus Nagel (Qualcomm) · Rana Ali Amjad (Qualcomm) · Ying Wang (Qualcomm) · Tijmen Blankevoort (Qualcomm) · Max Welling (University of Amsterdam / Qualcomm AI Research)

7、Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot

Jingtong Su (Peking University) · Yihang Chen (Peking University) · Tianle Cai (Peking University) · Tianhao Wu (Peking University) · Ruiqi Gao (Peking University) · Liwei Wang (Peking University) · Jason Lee (Princeton University)

8、Scientific Control for Reliable Neural Network Pruning

Yehui Tang (Peking University) · Yunhe Wang (Huawei Noah's Ark Lab) · Yixing Xu (Huawei Noah's Ark Lab) · Dacheng Tao (University of Sydney) · Chunjing XU (Huawei Technologies) · Chao Xu (Peking University) · Chang Xu (University of Sydney)

9、Neuron-level Structured Pruning using Polarization Regularizer

Tao Zhuang (Alibaba Group) · Zhixuan Zhang (Beijing University of Posts and Telecommunications) · Yuheng Huang (Beijing Univ. of Posts and Telecommunications) · Xiaoyi Zeng (Alibaba Group) · Kai Shuang (State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.) · Xiang Li (Alibaba Group)

10、Directional Pruning of Deep Neural Networks

Shih-Kang Chao (University of Missouri) · Zhanyu Wang (Purdue University) · Yue Xing (Purdue University) · Guang Cheng (Purdue University)

11、Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning

Jianda Chen (Nanyang Technological University) · Shangyu Chen (Nanyang Technological University, Singapore) · Sinno Jialin Pan (Nanyang Technological University, Singapore)

12、Movement Pruning: Adaptive Sparsity by Fine-Tuning

Victor Sanh (Hugging Face 🤗) · Thomas Wolf (Hugging Face) · Alexander Rush (Cornell University)

13、The Generalization-Stability Tradeoff In Neural Network Pruning

Brian Bartoldson (Florida State University) · Ari Morcos (Facebook AI Research) · Adrian Barbu (Florida State University, USA) · Gordon Erlebacher (Florida State University)

14、Bayesian Bits: Unifying Quantization and Pruning

Mart van Baalen (Qualcomm) · Christos Louizos (Qualcomm AI Research) · Markus Nagel (Qualcomm) · Rana Ali Amjad (Qualcomm) · Ying Wang (Qualcomm) · Tijmen Blankevoort (Qualcomm) · Max Welling (University of Amsterdam / Qualcomm AI Research)


压缩:10 篇


1、Universally Quantized Neural Compression

Eirikur Agustsson (Google) · Lucas Theis (Twitter)

Chong Yu (NVIDIA) · Chong Yu (Intel)

2、High-Fidelity Generative Image Compression

Fabian Mentzer (ETH Zurich) · George D Toderici (Google) · Michael Tschannen (Google Brain) · Eirikur Agustsson (Google)

3、MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models

Sourav Biswas (University of Waterloo) · Jerry Liu (Uber ATG) · Kelvin Wong (University of Toronto) · Shenlong Wang (University of Toronto) · Raquel Urtasun (Uber ATG)

4、Improving Inference for Neural Image Compression

Yibo Yang (University of California, Irivine) · Robert Bamler (University of California at Irvine) · Stephan Mandt (University of California, Irivine)

5、Practical Low-Rank Communication Compression in Decentralized Deep Learning

Thijs Vogels (EPFL) · Sai Praneeth Karimireddy (EPFL) · Martin Jaggi (EPFL)

6、Attribution Preservation in Network Compression for Reliable Network Interpretation

Geondo Park (Korea Advanced Institute of Science and Technology) · June Yong Yang (Korea Advanced Institute of Science and Technology) · Sung Ju Hwang (KAIST, AITRICS) · Eunho Yang (Korea Advanced Institute of Science and Technology; AItrics)

7、Self-Supervised Generative Adversarial Compression

Chong Yu (NVIDIA) · Chong Yu (Intel)

8、ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

Chia-Yu Chen (IBM research) · Jiamin Ni (IBM) · Songtao Lu (IBM) · Xiaodong Cui (IBM T. J. Watson Research Center) · Pin-Yu Chen (IBM Research AI) · Xiao Sun (IBM Thomas J. Watson Research Center) · Naigang Wang (IBM T. J. Watson Research Center) · Swagath Venkataramani (IBM Research) · Vijayalakshmi (Viji) Srinivasan (IBM TJ Watson) · Wei Zhang (IBM T.J.Watson Research Center) · Kailash Gopalakrishnan (IBM Research)

9、MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Wenhui Wang (MSRA) · Furu Wei (Microsoft Research Asia) · Li Dong (Microsoft Research) · Hangbo Bao (Harbin Institute of Technology) · Nan Yang (Microsoft Research Asia) · Ming Zhou (Microsoft Research)

WoodFisher: Efficient Second-Order Approximation for Neural NetworkCompression

Sidak Pal Singh (EPFL) · Dan Alistarh (IST Austria & Neural Magic Inc.)


NAS:12 篇


 1、ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding

Yibo Yang (Peking University) · Hongyang Li (Peking University) · Shan You (SenseTime) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Zhouchen Lin (Peking University)

2、BRP-NAS: Prediction-based NAS using GCNs

Thomas Chau (Samsung AI Center Cambridge) · Lukasz Dudziak (Samsung AI Center Cambridge) · Mohamed Abdelfattah (Samsung AI Centre Cambridge) · Royson Lee (Samsung AI Center Cambridge) · Hyeji Kim (Samsung AI Center Cambridge) · Nicholas Lane (Samsung AI Center Cambridge & University of Oxford)

3、Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS

Han Shi (Hong Kong University of Science and Technology) · Renjie Pi (Huawei Noah’s Ark Lab) · Hang Xu (Huawei Noah's Ark Lab) · Zhenguo Li (Noah's Ark Lab, Huawei Tech Investment Co Ltd) · James Kwok (Hong Kong University of Science and Technology) · Tong Zhang (Hong Kong University of Science and Technology)

4、CryptoNAS: Private Inference on a ReLU Budget

Zahra Ghodsi (New York University) · Akshaj Kumar Veldanda (New York University) · Brandon Reagen (New York University) · Siddharth Garg (NYU)

5、ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding

Yibo Yang (Peking University) · Hongyang Li (Peking University) · Shan You (SenseTime) · Fei Wang (SenseTime) · Chen Qian (SenseTime) · Zhouchen Lin (Peking University)

6、BRP-NAS: Prediction-based NAS using GCNs

Thomas Chau (Samsung AI Center Cambridge) · Lukasz Dudziak (Samsung AI Center Cambridge) · Mohamed Abdelfattah (Samsung AI Centre Cambridge) · Royson Lee (Samsung AI Center Cambridge) · Hyeji Kim (Samsung AI Center Cambridge) · Nicholas Lane (Samsung AI Center Cambridge & University of Oxford)

7、Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

Miao Zhang (UTS&BIT) · Huiqi Li (Beijing Institute of Technology) · Shirui Pan (Monash University) · Xiaojun Chang (Monash University) · Zongyuan Ge (Monash University) · Steven Su (University of Technology Sydney)

8、Hierarchical Neural Architecture Search for Deep Stereo Matching

Xuelian Cheng (Monash University) · Yiran Zhong (Australian National University) · Mehrtash T Harandi (Monash University) · Yuchao Dai (Northwestern Polytechnical University) · Xiaojun Chang (Monash University) · Hongdong Li (Australian National University) · Tom Drummond (Monash University) · Zongyuan Ge (Monash University)

9、A Study on Encodings for Neural Architecture Search

Colin White (RealityEngines.AI) · Willie Neiswanger (Carnegie Mellon University) · Sam Nolen (RealityEngines.AI) · Yash Savani (RealityEngines.AI)

10、Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search

Houwen Peng (Microsoft Research) · Hao Du (Microsoft Research) · Hongyuan Yu (MSRA) · QI LI (Tsinghua Univeristy) · Jing Liao (City University of Hong Kong) · Jianlong Fu (Microsoft Research)

11、CLEARER: Multi-Scale Neural Architecture Search for Image Restoration

Yuanbiao Gou (College of Computer Science, Sichuan University) · Boyun Li (College of Computer Science, Sichuan University) · Zitao Liu (TAL AI Lab) · Songfan Yang (TAL AI Lab) · Xi Peng (Institute for Infocomm, Research Agency for Science, Technology and Research (A*STAR) Singapore)

12、Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?

Shen Yan (Michigan State University) · Yu Zheng (Michigan State University) · Wei Ao (Michigan State University) · Xiao Zeng (Michigan State University) · Mi Zhang (Michigan State University)


8


优化

贝叶斯优化:14 篇


1、Bayesian Optimization over String Spaces

Henry Moss (Lancaster University) · David Leslie (Lancaster University and PROWLER.io) · Daniel Beck (University of Melbourne) · Javier Gonzalez (Amazon.com) · Paul Rayson (Lancaster University)

2、Bayesian Optimization of Risk Measures

Sait Cakmak (Georgia Institute of Technology) · Raul Astudillo Marban (Cornell University) · Peter Frazier (Cornell / Uber) · Enlu Zhou (Georgia Institute of Technology)

3、Bayesian Optimization for Iterative Learning

Vu Nguyen (University of Oxford) · Sebastian Schulze (University of Oxford) · Michael A Osborne (U Oxford)

4、Modular Bayesian Optimization with BoTorch: An Efficient Differentiable Monte-Carlo Approach

Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Daniel Jiang (Facebook) · Samuel Daulton (Facebook) · Ben Letham (Facebook) · Andrew Gordon Wilson (New York University)· Eytan Bakshy (Facebook)

5、Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Samuel Daulton (Facebook) · Maximilian Balandat (Facebook) · Eytan Bakshy (Facebook)

6、Federated Bayesian Optimization via Thompson Sampling

Zhongxiang Dai (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Patrick Jaillet (MIT)

7、Multi-Fidelity Bayesian Optimization via Deep Neural Networks

Shibo Li (University of Utah) · Wei Xing (University of Utah) · Robert Kirby (University of Utah) · Shandian Zhe (University of Utah)

8、Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Mina Konakovic Lukovic (Massachusetts Institute of Technology) · Yunsheng Tian (Massachusetts Institute of Technology) · Wojciech Matusik (MIT)

9、Fast Matrix Square Roots with Applications to Gaussian Processes andBayesian Optimization

Geoff Pleiss (Columbia University) · Martin Jankowiak (Uber AI Labs) · David Eriksson (Facebook) · Anil Damle (Cornell University) · Jacob Gardner (University of Pennsylvania)

10、Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Ben Letham (Facebook) · Roberto Calandra (Facebook AI Research) · Akshara Rai (Facebook) · Eytan Bakshy (Facebook)

11、Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Shali Jiang (Washington University in St. Louis) · Daniel Jiang (Facebook) · Maximilian Balandat (Facebook) · Brian Karrer (Facebook) · Jacob Gardner (University of Pennsylvania) · Roman Garnett (Washington University in St. Louis)

12、Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

Sreejith Balakrishnan (National University of Singapore) · Quoc Phong Nguyen (National University of Singapore) · Bryan Kian Hsiang Low (National University of Singapore) · Harold Soh (National University Singapore)

13、High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds

Noémie Jaquier (Karlsruhe Institute of Technology) · Leonel Rozo (Bosch Center for Artificial Intelligence)

14、High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Qing Feng (Facebook) · Ben Letham (Facebook) · Hongzi Mao (MIT) · Eytan Bakshy (Facebook)


15、Bayesian Robust Optimization for Imitation Learning

Daniel Brown (The University of Texas at Austin) · Scott Niekum (UT Austin) · Marek Petrik (University of New Hampshire)

凸优化:10 篇


1、Convex optimization based on global lower second-order models

Nikita Doikov (Catholic University of Louvain) · Yurii Nesterov (Catholic University of Louvain (UCL))

2、A convex optimization formulation for multivariate regression

Yunzhang Zhu (Ohio State University)

3、Optimal Query Complexity of Secure Stochastic Convex Optimization

Wei Tang (Washington University in St.Louis) · Chien-Ju Ho (Washington University in St. Louis) · Yang Liu (UC Santa Cruz)

4、Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

Benjamin Aubin (Ipht Saclay) · Florent Krzakala (ENS Paris, Sorbonnes Université & EPFL) · Yue Lu (Harvard University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

5、Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study

Assaf Dauber (Tel-Aviv University) · Meir Feder (Tel-Aviv University) · Tomer Koren (Google) · Roi Livni (Tel Aviv University)

6、Leveraging predictions in smoothed online convex optimization via gradient-based algorithms

Yingying Li (Harvard University) · Na Li (Harvard University)

7、An efficient nonconvex reformulation of stagewise convex optimizationproblems

Srinadh Bhojanapalli (Google AI) · Rudy Bunel (Deepmind) · Krishnamurthy Dvijotham (DeepMind) · Oliver Hinder (University of Pittsburgh)

8、Understanding spiking networks through convex optimization

Allan Mancoo (Champalimaud Centre for the Unknown) · Sander Keemink (Champalimaud Centre for the Unknown) · Christian K Machens (Champalimaud Centre for the Unknown)

9、Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition

Lin Chen (University of California, Berkeley) · Qian Yu (University of Southern California) · Hannah Lawrence (Flatiron Institute) · Amin Karbasi (Yale)

10、Online Convex Optimization Over Erdos-Renyi Random Networks

Jinlong Lei (Tongji University) · Peng Yi (Tongji University) · Yiguang Hong (Academy of Mathematics and Systems Science, Chinese Academy of Sciences) · Jie Chen (Beijing Institute of Technology) · Guodong Shi (University of Sydney)


凸优化:4 篇


 1、Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking

Isidoros Tziotis (UT Austin) · Constantine Caramanis (UT Austin) · Aryan Mokhtari (UT Austin)

2、Improved Analysis of Clipping Algorithms for Non-convex Optimization

Bohang Zhang (Peking University) · Jikai Jin (Peking University) · Cong Fang (Peking University) · Liwei Wang (Peking University)

3、Online Non-Convex Optimization with Inexact Models

Amélie Héliou (Criteo AI Lab) · Matthieu Martin (Criteo) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Thibaud J Rahier (INRIA)

4、Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization

Xuefeng GAO (The Chinese University of Hong Kong) · Mert Gurbuzbalaban (Rutgers) · Lingjiong Zhu (Florida State University)

1、Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking


Isidoros Tziotis (UT Austin) · Constantine Caramanis (UT Austin) · Aryan Mokhtari (UT Austin)

2、Improved Analysis of Clipping Algorithms for Non-convex Optimization

Bohang Zhang (Peking University) · Jikai Jin (Peking University) · Cong Fang (Peking University) · Liwei Wang (Peking University)

3、Online Non-Convex Optimization with Inexact Models

Amélie Héliou (Criteo AI Lab) · Matthieu Martin (Criteo) · Panayotis Mertikopoulos (CNRS (French National Center for Scientific Research)) · Thibaud J Rahier (INRIA)

4、Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization

Xuefeng GAO (The Chinese University of Hong Kong) · Mert Gurbuzbalaban (Rutgers) · Lingjiong Zhu (Florida State University)


5、Finding Second-Order Stationary Points Efficiently in Smooth NonconvexLinearly Constrained Optimization Problems

Songtao Lu (IBM Research) · Meisam Razaviyayn (University of Southern California) · Bo Yang (University of Minnesota) · Kejun Huang (University of Florida) · Mingyi Hong (University of Minnesota)


策略优化:7 篇


1、POMO: Policy Optimization with Multiple Optima for Reinforcement Learning


Yeong-Dae Kwon (Samsung SDS) · Jinho Choo (Samsung SDS) · Byoungjip Kim (Samsung SDS) · Iljoo Yoon (Samsung SDS) · Youngjune Gwon (Samsung SDS) · Seungjai Min (Samsung SDS)

2、Model-based Policy Optimization with Unsupervised Model Adaptation

Jian Shen (Shanghai Jiao Tong University) · Han Zhao (Carnegie Mellon University) · Weinan Zhang (Shanghai Jiao Tong University) · Yong Yu (Shanghai Jiao Tong Unviersity)

3、Dynamic Regret of Policy Optimization in Non-stationary Environments

Yingjie Fei (Cornell University) · Zhuoran Yang (Princeton) · Zhaoran Wang (Northwestern University) · Qiaomin Xie (Cornell University)

4、MOPO: Model-based Offline Policy Optimization

Tianhe Yu (Stanford University) · Garrett W. Thomas (Stanford University) · Lantao Yu (Stanford University) · Stefano Ermon (Stanford) · James Zou (Stanford University) · Sergey Levine (UC Berkeley) · Chelsea Finn (Stanford) · Tengyu Ma (Stanford University)

5、Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Paul Barde (Quebec AI institute - Ubisoft La Forge) · Julien Roy (Mila) · Wonseok Jeon (MILA, McGill University) · Joelle Pineau (McGill University) · Chris Pal (MILA, Polytechnique Montréal, Element AI) · Derek Nowrouzezahrai (McGill University)

6、Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization

Nan Jiang (University of Illinois at Urbana-Champaign) · Jiawei Huang (University of Illinois at Urbana-Champaign)

7、How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

Pierluca D'Oro (MILA) · Wojciech Jaśkowski (NNAISENSE SA)


 其他优化:57 篇


1、Black-B ox Optimization with Local Generative Surrogates

Sergey Shirobokov (Imperial College London) · Vladislav Belavin (National Research University Higher School of Economics) · Michael Kagan (SLAC / Stanford) · Andrei Ustyuzhanin (National Research University Higher School of Economics) · Atilim Gunes Baydin (University of Oxford)

2、Scalable Black-box Optimization by Learnable Search Space Partition

Linnan Wang (Brown University) · Rodrigo Fonseca (Brown University) · Yuandong Tian (Facebook AI Research)

3、Improving model calibration with accuracy versus uncertainty optimization

Ranganath Krishnan (Intel Labs) · Omesh Tickoo (Intel)

4、Stochastic Optimization for Performative Prediction

Celestine Mendler-Dünner (UC Berkeley) · Juan Perdomo (University of California, Berkeley) · Tijana Zrnic (UC Berkeley) · Moritz Hardt (University of California, Berkeley)

5、Unfolding the Alternating Optimization for Blind Super Resolution

zhengxiong luo (中国科学院自动化所) · Yan Huang (CRIPAC, CASIA) · Shang Li (CASIA) · Liang Wang (NLPR, China) · Tieniu Tan (Chinese Academy of Sciences)

6、Dual-Free Stochastic Decentralized Optimization with Variance Reduction

Hadrien Hendrikx (INRIA - PSL) · Francis Bach (INRIA - Ecole Normale Superieure) · Laurent Massoulié (Inria)

7、Black-Box Certification with Randomized Smoothing: A FunctionalOptimization Based Framework

Dinghuai Zhang (Peking University) · Mao Ye (The University of Texas at Austin) · Chengyue Gong (Peking University) · Zhanxing Zhu (Peking University) · Qiang Liu (UT Austin)

8、Projection Robust Wasserstein Distance and Riemannian Optimization

Tianyi Lin (UC Berkeley) · Chenyou Fan (The Chinese University of Hong Kong, Shenzhen) · Nhat Ho (University of Texas at Austin) · Marco Cuturi (Google Brain & CREST - ENSAE) · Michael Jordan (UC Berkeley)

9、Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits

Jack Parker-Holder (University of Oxford) · Vu Nguyen (University of Oxford) · Stephen J Roberts (University of Oxford)

10、Coresets via Bilevel Optimization for Continual Learning and Streaming

Zalán Borsos (ETH Zurich) · Mojmir Mutny (ETH Zurich) · Andreas Krause (ETH Zurich)

11、Semialgebraic Optimization for Lipschitz Constants of ReLU Networks

Tong Chen (LAAS-CNRS) · Jean B Lasserre (lasserre@laas.fr) · Victor Magron (LAAS-CNRS) · Edouard Pauwels (IRIT)

Stochastic Variance Reduced Accelerated Dual Averaging for Finite-SumOptimization

Chaobing Song (Tsinghua University) · Yong Jiang (Tsinghua) · Yi Ma (UC Berkeley)

12、Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation

Jianfeng Zhang (NUS) · Xuecheng Nie (NUS) · Jiashi Feng (National University of Singapore)

13、Robust Optimization for Fairness with Noisy Protected Groups

Serena Wang (Google) · Wenshuo Guo (UC Berkeley) · Harikrishna Narasimhan (Google Research) · Andrew Cotter (Google) · Maya Gupta (Google) · Michael Jordan (UC Berkeley)

13、FedSplit: an algorithmic framework for fast federated optimization

Reese Pathak (University of California, Berkeley) · Martin Wainwright (UC Berkeley)

14、Robust, Accurate Stochastic Optimization for Variational Inference

Akash Kumar Dhaka (Aalto University) · Alejandro Catalina (Aalto University) · Michael Andersen (Aalto University) · Måns Magnusson (Aalto University) · Jonathan Huggins (Boston University) · Aki Vehtari (Aalto University)

15、First Order Constrained Optimization in Policy Space

Yiming Zhang (New York University) · Quan Vuong (University of California, San Diego) · Keith Ross (NYU Shanghai)

16、Reinforced Molecular Optimization with Neighborhood-Controlled Grammars

Chencheng Xu (Tsinghua University) · Qiao Liu (Tsinghua University) · Minlie Huang (Tsinghua University) · Tao Jiang (University of California - Riverside)

17、Advances in Black-Box VI: Normalizing Flows, Importance Weighting, andOptimization

Abhinav Agrawal (UMass Amherst) · Daniel Sheldon (University of Massachusetts Amherst) · Justin Domke (University of Massachusetts, Amherst)

18、Improved Algorithms for Convex-Concave Minimax Optimization

Yuanhao Wang (Tsinghua University) · Jian Li (Tsinghua University)

19、Biased Stochastic Gradient Descent for Conditional Stochastic Optimization

Yifan Hu (University of Illinois at Urbana-Champaign) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Xin Chen (University of Illinois at Urbana-Champaign) · Niao He (UIUC)

20、Modeling and Optimization Trade-off in Meta-learning

Katelyn Gao (Intel Labs) · Ozan Sener (Intel Labs)

21、Online Optimization with Memory and Competitive Control

Guanya Shi (Caltech) · Yiheng Lin (California Institute of Technology) · Soon-Jo Chung (Caltech) · Yisong Yue (Caltech) · Adam Wierman (California Institute of Technology)

22、Automatically Learning Compact Quality-aware Surrogates forOptimization Problems

Kai Wang (Harvard University) · Bryan Wilder (Harvard University) · Andrew Perrault (Harvard University) · Milind Tambe (Harvard University/Google)

23、Bayesian filtering unifies adaptive and non-adaptive neural networkoptimization methods

Laurence Aitchison (University of Cambridge)

24、Model Inversion Networks for Model-Based Optimization

Aviral Kumar (UC Berkeley) · Sergey Levine (UC Berkeley)

25、Neural Architecture Generator Optimization

Robin Ru (Oxford University) · Pedro M Esperança (Huawei) · Fabio Maria Carlucci (Sapienza University of Rome)

26、Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Austin Tripp (University of Cambridge) · Erik Daxberger (University of Cambridge) · José Miguel Hernández-Lobato (University of Cambridge)

27、Guiding Deep Molecular Optimization with Genetic Exploration

Sung-Soo Ahn (KAIST) · Junsu Kim (KAIST) · Hankook Lee (Korea Advanced Institute of Science and Technology) · Jinwoo Shin (KAIST)

28、Relative gradient optimization of the Jacobian term in unsupervised deep learning

Luigi Gresele (MPI for Intelligent Systems, Tübingen) · Giancarlo Fissore (Inria) · Adrián Javaloy (Saarland University) · Bernhard Schölkopf (MPI for Intelligent Systems) · Aapo Hyvarinen (University of Helsinki)

29、Fully Dynamic Algorithm for Constrained Submodular Optimization

Silvio Lattanzi (Google Research) · Slobodan Mitrović (MIT) · Ashkan Norouzi-Fard (Google Research) · Jakub Tarnawski (Microsoft Research) · Morteza Zadimoghaddam (Google Research)

30、Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices

John Duchi (Stanford) · Oliver Hinder (University of Pittsburgh) · Andrew Naber (Stanford University) · Yinyu Ye (Standord)

31、Cooperative Multi-Player Bandit Optimization

Ilai Bistritz (Stanford) · Nicholas Bambos (Stanford University)

32、Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits

Arya Akhavan (ENSAE - IIT) · Massimiliano Pontil (IIT & UCL) · Alexandre Tsybakov (CREST, ENSAE)

33、Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

Joey Huchette (Rice University) · Haihao Lu (University of Chicago) · Hossein Esfandiari (Google Research) · Vahab Mirrokni (Google Research NYC)

34、Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes

Ayoub El Hanchi (McGill University) · David Stephens (McGill University)

35、Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping

Eduard Gorbunov (Moscow Institute of Physics and Technology) · Marina Danilova (ICS RAS) · Alexander Gasnikov (MIPT & HSE)

36、Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

Stefano Sarao Mannelli (Institut de Physique Théorique) · Eric Vanden-Eijnden (New York University) · Lenka Zdeborová (University Paris-Saclay & EPFL)

37、Acceleration with a Ball Optimization Oracle

Yair Carmon (Stanford University) · Arun Jambulapati (Stanford University) · Qijia Jiang (Stanford University) · Yujia Jin (Stanford University) · Yin Tat Lee (UW) · Aaron Sidford (Stanford) · Kevin Tian (Stanford University

38、Large-Scale Methods for Distributionally Robust Optimization

Daniel Levy (Stanford University) · Yair Carmon (Stanford University) · John Duchi (Stanford) · Aaron Sidford (Stanford)

39、DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks

Dennis Wei (IBM Research) · Tian Gao (IBM Research AI) · yue yu (Lehigh University)

40、Online Linear Optimization with Many Hints

Aditya Bhaskara (University of Utah) · Ashok Cutkosky (Google Research) · Ravi Kumar (Google) · Manish Purohit (Google)

41、Tackling the Objective Inconsistency Problem in Heterogeneous FederatedOptimization

Jianyu Wang (Carnegie Mellon University) · Qinghua Liu (Princeton University) · Hao Liang (Carnegie Mellon University) · Gauri Joshi (Carnegie Mellon University) · H. Vincent Poor (Princeton University)

42、Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization

Michal Derezinski (UC Berkeley) · Burak Bartan (Stanford University) · Mert Pilanci (Stanford) · Michael W Mahoney (UC Berkeley)

43、A Catalyst Framework for Minimax Optimization

Junchi Yang (University of Illinois) · Siqi Zhang (University of Illinois at Urbana-Champaign) · Negar Kiyavash (École Polytechnique Fédérale de Lausanne) · Niao He (UIUC)

44、A Novel Approach for Constrained Optimization in Graphical Models

Sara Rouhani (University of Texas at Dallas) · Tahrima Rahman (UT Dallas) · Vibhav Gogate (UT Dallas)

45、Network Pruning via Greedy Optimization: Fast Rate and Efficient Algorithms

Mao Ye (The University of Texas at Austin) · Lemeng Wu (UT Austin) · Qiang Liu (UT Austin)

Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization

Jonathan Lacotte (Stanford University) · Mert Pilanci (Stanford)

46、Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate

Zhiyuan Li (Princeton University) · Kaifeng Lyu (Tsinghua University) · Sanjeev Arora (Princeton University)

47、Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

Vu Nguyen (University of Oxford) · Vaden Masrani (University of British Columbia) · Rob Brekelmans (University of Southern California) · Michael A Osborne (U Oxford) · Frank Wood (University of British Columbia)

48、Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-MaxOptimization

Yan Yan (the University of Iowa) · Yi Xu (Alibaba Group U.S. Inc.) · Qihang Lin (University of Iowa) · Wei Liu (Tencent AI Lab) · Tianbao Yang (The University of Iowa)

49、Erdos Goes Neural: an Unsupervised Learning Framework for CombinatorialOptimization on Graphs

Nikolaos Karalias (EPFL) · Andreas Loukas (EPFL)

50、Stochastic Optimization with Laggard Data Pipelines

Naman Agarwal (Google) · Rohan Anil (Google) · Tomer Koren (Google) · Kunal Talwar (Google) · Cyril Zhang (Princeton University)

51、A Feasible Level Proximal Point Method for Nonconvex Sparse ConstrainedOptimization

Digvijay Boob (Georgia Institute of Technology) · Qi Deng (Shanghai University of Finance and Economics) · Guanghui Lan (Georgia Tech) · Yilin Wang (Shanghai University of Finance and Economics)

52、Conformal Symplectic and Relativistic Optimization

Guilherme Starvaggi Franca (University of California, Berkeley) · Jeremias Sulam (Johns Hopkins University) · Daniel Robinson (Johns Hopkins University) · Rene Vidal (Johns Hopkins University, USA)

53、Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces

Guy Lorberbom (Technion) · Chris J. Maddison (University of Toronto) · Nicolas Heess (Google DeepMind) · Tamir Hazan (Technion) · Daniel Tarlow (Google Brain)

54、A Simple and Efficient Smoothing Method for Accelerated Optimizationand Local Exploration

Kevin Scaman (Noah's Ark Lab, Huawei Technologies) · Ludovic DOS SANTOS (Huawei) · Merwan Barlier (Huawei Technologies) · Igor Colin (Huawei)

55、Delta-STN: Efficient Bilevel Optimization of Neural Networks using Structured Response Jacobians

Juhan Bae (University of Toronto) · Roger Grosse (University of Toronto)

56、Finer Metagenomic Reconstruction via Biodiversity Optimization

Simon Foucart (Texas A&M) · David Koslicki (Pennsylvania State University)

57、Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization

Dmitry Koralev (KAUST) · Adil SALIM (KAUST) · Peter Richtarik (KAUST)



9


可解释相关

Interpretable:10 篇


1、Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations


Rohan Paleja (Georgia Institute of Technology) · Andrew Silva (Georgia Institute of Technology) · Letian Chen (Georgia Institute of Technology) · Matthew Gombolay (Georgia Institute of Technology)

2、Interpretable Sequence Learning for Covid-19 Forecasting

Sercan Arik (Google) · Chun-Liang Li (Google) · Martin Nikoltchev (Google) · Rajarishi Sinha (Google) · Arkady Epshteyn (Google) · Jinsung Yoon (Google) · Long Le (Google) · Vikas Menon (Google) · Shashank Singh (Google) · Yash Sonthalia (Google) · Hootan Nakhost (Google) · Leyou Zhang (Google) · Elli Kanal (Google) · Tomas Pfister (Google)

3、Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Wanqian Yang (Harvard University) · Lars Lorch (Harvard) · Moritz Graule (Harvard University) · Himabindu Lakkaraju (Harvard) · Finale Doshi-Velez (Harvard)

4、ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping

Cher Bass (King's College London) · Mariana da Silva (King's College London) · Carole Sudre (King's College London) · Petru-Daniel Tudosiu (King's College London) · Stephen Smith (FMRIB Centre - University of Oxford) · Emma Robinson (King's College)

5、How does this interaction affect me? Interpretable attribution for feature interactions

Michael Tsang (University of Southern California) · Sirisha Rambhatla (University of Southern California) · Yan Liu (University of Southern California)

6、Learning outside the Black-Box: The pursuit of interpretable models

Jonathan Crabbe (University of Cambridge) · Yao Zhang (University of Cambridge) · William Zame (UCLA) · Mihaela van der Schaar (University of Cambridge)

7、GANSpace: Discovering Interpretable GAN Controls

Erik Härkönen (Aalto University) · Aaron Hertzmann (Adobe) · Jaakko Lehtinen (Aalto University & NVIDIA) · Sylvain Paris (Adobe)

8、Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech

Shailee Jain (The University of Texas at Austin) · Vy Vo (Intel Corporation) · Shivangi Mahto (The University of Texas at Austin) · Amanda LeBel (The University of Texas at Austin) · Javier Turek (Intel Labs) · Alexander Huth (The University of Texas at Austin)

9、Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE

Ding Zhou (Columbia University) · Xue-Xin Wei (University of Pennsylvania)

10、Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

Wangchunshu Zhou (Beihang University) · Jinyi Hu (Tsinghua University) · Hanlin Zhang (South China University of Technology) · Xiaodan Liang (Sun Yat-sen University) · Maosong Sun (Tsinghua University) · Chenyan Xiong (Microsoft Research AI) · Jian Tang (Mila)


Explanation:10 篇


1、Decisions, Counterfactual Explanations and Strategic Behavior

Stratis Tsirtsis (MPI-SWS) · Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)

2、Model Agnostic Multilevel Explanations

Karthikeyan Natesan Ramamurthy (IBM Research) · Bhanukiran Vinzamuri (IBM Research) · Yunfeng Zhang (IBM Research) · Amit Dhurandhar (IBM Research)

3、PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Minh N Vu (University of Florida) · My T. Thai (University of Florida)

4、On Completeness-aware Concept-Based Explanations in Deep Neural Networks

Chih-Kuan Yeh (Carnegie Mellon University) · Been Kim (Google) · Sercan Arik (Google) · Chun-Liang Li (Google) · Tomas Pfister (Google) · Pradeep Ravikumar (Carnegie Mellon University)

5、Debugging Tests for Model Explanations

Julius Adebayo (MIT) · Michael Muelly (Stanford University) · Ilaria Liccardi (MIT) · Been Kim (Google)

6、How Can I Explain This to You? An Empirical Study of Deep Neural NetworkExplanation Methods

Jeya Vikranth Jeyakumar (University of California, Los Angeles) · Joseph Noor (University of California, Los Angeles) · Yu-Hsi Cheng (UCLA) · Luis Garcia (University of California, Los Angeles) · Mani Srivastava (UCLA)

7、Towards Interpretable Natural Language Understanding with Explanationsas Latent Variables

Wangchunshu Zhou (Beihang University) · Jinyi Hu (Tsinghua University) · Hanlin Zhang (South China University of Technology) · Xiaodan Liang (Sun Yat-sen University) · Maosong Sun (Tsinghua University) · Chenyan Xiong (Microsoft Research AI) · Jian Tang (Mila)

8、Generative causal explanations of black-box classifiers

Matthew O'Shaughnessy (Georgia Tech) · Gregory Canal (Georgia Institute of Technology) · Marissa Connor (Georgia Tech) · Christopher Rozell (Georgia Institute of Technology) · Mark Davenport (Georgia Institute of Technology)

9、Compositional Explanations of Neurons

Jesse Mu (Stanford University) · Jacob Andreas (MIT)

10、Learning Global Transparent Models consistent with Local ContrastiveExplanations

Tejaswini Pedapati (IBM Research) · Avinash Balakrishnan (IBM) · Karthikeyan Shanmugam (IBM Research, NY) · Amit Dhurandhar (IBM Research)

NeurIPS 2020 论文接收列表已出,欢迎大家投稿让更多的人了解你们的工作~

来源:AI科技评论

AMiner学术头条
AMiner学术头条

AMiner平台由清华大学计算机系研发,拥有我国完全自主知识产权。系统2006年上线,吸引了全球220个国家/地区800多万独立IP访问,数据下载量230万次,年度访问量1000万,成为学术搜索和社会网络挖掘研究的重要数据和实验平台。

https://www.aminer.cn/
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入门论文NeurIPS 2020
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Li Dong人物

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半监督学习技术

半监督学习属于无监督学习(没有任何标记的训练数据)和监督学习(完全标记的训练数据)之间。许多机器学习研究人员发现,将未标记数据与少量标记数据结合使用可以显着提高学习准确性。对于学习问题的标记数据的获取通常需要熟练的人类代理(例如转录音频片段)或物理实验(例如,确定蛋白质的3D结构或确定在特定位置处是否存在油)。因此与标签处理相关的成本可能使得完全标注的训练集不可行,而获取未标记的数据相对便宜。在这种情况下,半监督学习可能具有很大的实用价值。半监督学习对机器学习也是理论上的兴趣,也是人类学习的典范。

权重技术

线性模型中特征的系数,或深度网络中的边。训练线性模型的目标是确定每个特征的理想权重。如果权重为 0,则相应的特征对模型来说没有任何贡献。

凸优化技术

凸优化,或叫做凸最优化,凸最小化,是数学最优化的一个子领域,研究定义于凸集中的凸函数最小化的问题。凸优化在某种意义上说较一般情形的数学最优化问题要简单,譬如在凸优化中局部最优值必定是全局最优值。凸函数的凸性使得凸分析中的有力工具在最优化问题中得以应用,如次导数等。 凸优化应用于很多学科领域,诸如自动控制系统,信号处理,通讯和网络,电子电路设计,数据分析和建模,统计学(最优化设计),以及金融。在近来运算能力提高和最优化理论发展的背景下,一般的凸优化已经接近简单的线性规划一样直捷易行。许多最优化问题都可以转化成凸优化(凸最小化)问题,例如求凹函数f最大值的问题就等同于求凸函数 -f最小值的问题。

元学习技术

元学习是机器学习的一个子领域,是将自动学习算法应用于机器学习实验的元数据上。现在的 AI 系统可以通过大量时间和经验从头学习一项复杂技能。但是,我们如果想使智能体掌握多种技能、适应多种环境,则不应该从头开始在每一个环境中训练每一项技能,而是需要智能体通过对以往经验的再利用来学习如何学习多项新任务,因此我们不应该独立地训练每一个新任务。这种学习如何学习的方法,又叫元学习(meta-learning),是通往可持续学习多项新任务的多面智能体的必经之路。

高斯过程技术

迁移学习技术

迁移学习是一种机器学习方法,就是把为任务 A 开发的模型作为初始点,重新使用在为任务 B 开发模型的过程中。迁移学习是通过从已学习的相关任务中转移知识来改进学习的新任务,虽然大多数机器学习算法都是为了解决单个任务而设计的,但是促进迁移学习的算法的开发是机器学习社区持续关注的话题。 迁移学习对人类来说很常见,例如,我们可能会发现学习识别苹果可能有助于识别梨,或者学习弹奏电子琴可能有助于学习钢琴。

图神经网络技术

图网络即可以在社交网络或其它基于图形数据上运行的一般深度学习架构,它是一种基于图结构的广义神经网络。图网络一般是将底层图形作为计算图,并通过在整张图上传递、转换和聚合节点特征信息,从而学习神经网络基元以生成单节点嵌入向量。生成的节点嵌入向量可作为任何可微预测层的输入,并用于节点分类或预测节点之间的连接,完整的模型可以通过端到端的方式训练。

主动学习技术

主动学习是半监督机器学习的一个特例,其中学习算法能够交互式地查询用户(或其他信息源)以在新的数据点处获得期望的输出。 在统计学文献中,有时也称为最佳实验设计。

WGAN技术

就其本质而言,任何生成模型的目标都是让模型(习得地)的分布与真实数据之间的差异达到最小。然而,传统 GAN 中的判别器 D 并不会当模型与真实的分布重叠度不够时去提供足够的信息来估计这个差异度——这导致生成器得不到一个强有力的反馈信息(特别是在训练之初),此外生成器的稳定性也普遍不足。 Wasserstein GAN 在原来的基础之上添加了一些新的方法,让判别器 D 去拟合模型与真实分布之间的 Wasserstein 距离。Wassersterin 距离会大致估计出「调整一个分布去匹配另一个分布还需要多少工作」。此外,其定义的方式十分值得注意,它甚至可以适用于非重叠的分布。

强化学习技术

强化学习是一种试错方法,其目标是让软件智能体在特定环境中能够采取回报最大化的行为。强化学习在马尔可夫决策过程环境中主要使用的技术是动态规划(Dynamic Programming)。流行的强化学习方法包括自适应动态规划(ADP)、时间差分(TD)学习、状态-动作-回报-状态-动作(SARSA)算法、Q 学习、深度强化学习(DQN);其应用包括下棋类游戏、机器人控制和工作调度等。

旷视科技机构

北京旷视科技有限公司是一家行业领先的人工智能公司,在深度学习方面拥有核心竞争力。旷视向客户提供包括先进算法、平台软件、应用软件及内嵌人工智能功能的物联网设备的全栈式解决方案,并在多个行业取得领先地位。2017年和2019年,旷视跻身《麻省理工科技评论》发布的两项「50大最聪明公司」榜单中。 旷视是全球为数不多的拥有自主研发深度学习框架的公司之一,旷视自研的深度学习框架MegEngine作为旷视人工智能算法平台Brain++的核心组件,为算法训练、部署及模型改进过程提供重要支持。 旷视总部位于北京,拥有 2,000 多名员工,并在北京、上海、南京、成都等地都设有研发中心。旷视的典型客户包括金融科技公司、银行、智能手机公司、第三方系统集成商、物业管理者、学校、物流公司及制造商等。

https://www.megvii.com/
Anki机构

Anki 公司是由卡内基梅隆机器人研究所(Carnegie Mellon Robotics Institute)的三名毕业生在 2010 年创办的,现已获得了超过 2 亿美元的风险投资。更重要的是,它的产品确确实实吸引到了客户。Anki 目前已经售出了 150 万台机器人,并且他们找到了他们认为是最容易打入家庭市场的道路——玩具。这个明星产品是一个狂躁的小推土机机器人,名为 Cozmo,它可以在桌面上行走,玩简单的游戏,它的顶部装有会亮的立方体。根据一项分析,如果按照收入计算的话,Cozmo 是 2017 年美国、英国和法国的亚马逊网站上最畅销的玩具。 2017 年,Anki 公司就声称收入接近 1 亿美元了,当时 Anki 本可以进入「盈利」状态了,但它却将资金投入了一个 10 到 15 年的计划——一个从 Roomba 到 Rosie 的转变。

http://anki.com/
相关技术
知乎机构

作为中文互联网综合性内容平台,知乎将AI广泛应用与社区,构建了人、内容之间的多元连接,提升了社区的运转效率和用户体验。知乎通过内容生产、分发,社区治理等领域的AI应用,也创造了独有的技术优势和社区AI创新样本。

https://www.zhihu.com
联邦学习技术

如何在保护数据隐私、满足合法合规要求的前提下继续进行机器学习,这部分研究被称为「联邦学习」(Federated Learning)。

实例分割技术

实例分割是检测和描绘出现在图像中的每个不同目标物体的任务。

目标检测技术

一般目标检测(generic object detection)的目标是根据大量预定义的类别在自然图像中确定目标实例的位置,这是计算机视觉领域最基本和最有挑战性的问题之一。近些年兴起的深度学习技术是一种可从数据中直接学习特征表示的强大方法,并已经为一般目标检测领域带来了显著的突破性进展。

Tieniu Tan人物

谭铁牛, 英国帝国理工学院电子电气工程系图像处理专业博士研究生,中国科学院院士、英国皇家工程院外籍院士、发展中国家科学院院士和巴西科学院通讯院士。现任中央政府驻港联络办副主任、中国科学院自动化研究所研究员、博士生导师、智能感知与计算研究中心主任。

胶囊网络技术

简而言之,一个胶囊网络是由胶囊而不是由神经元构成。一个胶囊是一小群神经元,它们可以学习在一个图片的一定区域内检查一个特定的对象(比如,一个矩形)。它的输出是一个向量(例如,一个8维的向量)。每个向量的长度代表了物体是否存在的估计概率[1],它的方向(例如在8维空间里)记录了物体的姿态参数(比如,精确的位置、旋转等)。如果物体有稍微的变化(比如,移动、旋转、尺寸变化等),胶囊将也会输出一个长度相同但是方向稍微变化的向量。因此胶囊是等变的。

自监督学习技术

一个例子中的内容特别多,而用一个例子做一个任务,就等于把其他的内容浪费了,因此我们需要从一个样本中找出多个任务。比如说遮挡图片的一个特定部分,用没遮挡部分来猜遮挡的部分是一个任务。那么通过遮挡不同的部分,就可以用一个样本完成不同任务。Yann Lecun描述的这个方法被业界称作「自监督学习」

量化技术

深度学习中的量化是指,用低位宽数字的神经网络近似使用了浮点数的神经网络的过程。

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