Papers can be viewed by clicking on the corresponding titles.

The authors of contributed talks 1-3 will also be presenting their works in Poster Session I.

Poster Session I (08:40 - 09:40 AM, Dec 14, PST)

$f$-Mutual Information Contrastive Learning - [Contributed Talk 1] [Poster] [Zoom] - Guojun Zhang (University of Waterloo), Yiwei Lu (University of Waterloo), Sun Sun (National Research Council Canada), Hongyu Guo (National Research Council Canada), Yaoliang Yu (University of Waterloo)

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework - [Contributed Talk 2] [Poster] [Zoom] - Ching-Yun Ko (MIT), Jeet Mohapatra (MIT), Pin-Yu Chen (IBM Research AI), Sijia Liu (Michigan State University), Luca Daniel (Massachusetts Institute of Technology), Lily Weng (UCSD)

Mine your own view: A self-supervised approach for learning representations of neural activity - [Contributed Talk 3] [Poster] [Zoom] - Mehdi Azabou (Georgia Institute of Technology), Mohammad Gheshlaghi Azar (DeepMind), Ran Liu (Georgia Institute of Technology), Chi-Heng Lin (Georgia Tech), Erik C Johnson (Johns Hopkins Univeristy Applied Physics Laboratory), Kiran Bhaskaran-Nair (Washington University in St Louis), Max Dabagia (Georgia Institute of Technology), Bernardo Avila Pires (Deep Mind), Lindsey Kitchell (Johns Hopkins University Applied Physics Laboratory), Keith B Hengen ( Washington University in St Louis), William Gray Roncal (Johns Hopkins University Applied Physics Laboratory), Michal Valko (DeepMind), Eva Dyer (Georgia Tech)

Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations - [Contributed Talk 4] - Fangyu Liu (University of Cambridge), Yunlong Jiao (Amazon), Jordan Massiah (Amazon), Emine Yilmaz (University College London), Serhii Havrylov (Institute for Language, Cognition and Computation, the University of Edinburgh)

Enhancing Hyperbolic Graph Embeddings via Contrastive Learning - [Contributed Talk 5] - Jiahong Liu (Harbin Institute of Technology(Shenzhen)), Menglin Yang (The Chinese University of Hong Kong), Min Zhou (Huawei Technologies co. ltd), Shanshan Feng (Harbin Institute of Technology, Shenzhen), Philippe Fournier-Viger (Shenzhen University)

3D Infomax improves GNNs for Molecular Property Prediction - [Poster] [Zoom] - Hannes Stärk (Technical University of Munich), Dominique Beani (InVivo AI), Gabriele Corso (MIT), Prudencio Tossou (Valence Discovery)

Focused Contrastive Training for Test-based Constituency Analysis - [Poster] [Zoom] - Benjamin Roth (University of Vienna), Erion Çano (University of Vienna)

A Comparative Analysis of Semi-Supervised and Self-Supervised Classification for Labeling Tweets about Police Brutality - [Poster] [Zoom] - Wuraola Oyewusi (Data Science Nigeria), Elaine Nsoesie (Boston University), Opeyemi M Osakuade (Data Science Nigeria), Olubayo Adekanmbi (Data Science Nigeria)

Stochastic Contrastive Learning - [Poster] [Zoom] - Jason Ramapuram (Apple Inc), Dan Busbridge (Apple), Xavier Suau Cuadros (Apple Inc.), Russ Webb (Apple)

Domain-Agnostic Clustering with Self-Distillation - [Poster] [Zoom] - Mohammed Adnan (University of Guelph/Vector Institute), Yani A Ioannou (University of Calgary), Kenyon C.-Y. Tsai (Vector Institute), Graham Taylor (University of Guelph)

Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations - [Poster] [Zoom] - Semih Günel (EPFL), Florian Aymanns (EPFL), Sina Honari (EPFL), Pavan Ramdya (EPFL), Pascal Fua (EPFL, Switzerland)

Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE - [Poster] [Zoom] - Junya Chen (Duke University), Zhe Gan (Microsoft), Xuan Li (Virginia Tech), Qing Guo (Virginia Tech), Liqun Chen (Amazon), Shuyang Gao (Amazon), Tagyoung Chung (Amazon), Yi Xu (Amazon), Belinda Zeng (Amazon ), Wenlian Lu (Fudan University), Fan Li (Duke University), Lawrence Carin Duke (CS), Chenyang Tao (Duke University)

Towards Efficient and Effective Self-Supervised Learning of Visual Representations - [Poster] [Zoom] - Sravanti Addepalli (Indian Institute of Science), Kaushal S Bhogale (AI4Bharat), Priyam Dey (Indian Institute of Science), Venkatesh Babu RADHAKRISHNAN (Indian Institute of Science)

Self-Supervised GNN that Jointly Learns to Augment - [Poster] [Zoom] - Zekarias Tilahun Kefato (KTH Royal Institute of Technology), Sarunas Girdzijauskas (KTH Royal Institute of Technology), Hannes Stärk (Technical University of Munich)

ProtoSEED: Prototypical Self-SupervisedRepresentation Distillation - [Poster] [Zoom] - Kyungmin Lee (Agency for Defense Development)

Predicting Gaussian noise injection for self-supervised Generative Adversarial Nets - [Poster] [Zoom; pwd:80805] - Zhiyuan Wu (Technical University of Munich), Grigorios Chrysos (École Polytechnique Fédérale de Lausanne), Volkan Cevher (EPFL)

Learning from Mistakes: Using Mis-predictions as Harm Alerts in Language Pre-Training - [Poster] [Zoom] - Chen Xing (Salesforce Research), Wenhao Liu (Salesforce Metamind), Caiming Xiong (Salesforce Research)

Self-supervision of wearable sensors time-series data for influenza detection - [Poster] [Zoom] - Arinbjörn Kolbeinsson (Evidation), Piyusha Gade (Evidation), Raghu Kainkaryam (Evidation), Filip Jankovic (Evidation), Luca Foschini (Evidation)

Expansive Latent Space Trees for Planning from Visual Inputs - [Poster] [Zoom] - Robert Gieselmann (KTH Royal Institute of Technology), Florian T. Pokorny (KTH Royal Institute of Technology)

As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning - [Poster] [Zoom] - Fiorella Wever (University of Amsterdam), T Anderson Keller (University of Amsterdam), Laura Symul (Department of Statistics, Stanford University, California), Victor Garcia (University of Amsterdam)

Using self-supervision and augmentations to build insights into neural coding - [Poster] [Zoom] - Mehdi Azabou (Georgia Institute of Technology), Max Dabagia (Georgia Institute of Technology), Ran Liu (Georgia Institute of Technology), Chi-Heng Lin (Georgia Tech), Keith B Hengen ( Washington University in St Louis), Eva Dyer (Georgia Tech)

Temperature as Uncertainty in Contrastive Learning - [Poster] [Zoom] - Oliver Zhang (Stanford University), Mike H Wu (Stanford University), Jasmine Bayrooti (Stanford University), Noah Goodman (Stanford University)

Label Noise Resiliency with Self-supervised Representations - [Poster] [Zoom] - Zahra Vaseqi (McGill University), Ibtihel Amara (McGill University), Samrudhdhi B Rangrej (McGill University)

Pre-training Molecular Graph Representation with 3D Geometry -- Rethinking Self-Supervised Learning on Structured Data - [Poster] [Zoom] - Shengchao Liu (Mila, Université de Montréal), Hanchen Wang (University of Cambridge), Weiyang Liu (University of Cambridge), Joan Lasenby (University of Cambridge), Hongyu Guo (National Research Council Canada), Jian Tang (U Montreal)

Poster Session II (1:30 - 2:30 PM, Dec 14, PST)

Self-Supervised Learning for Molecular Property Prediction - [Poster] [Zoom] - Laurent Dillard (Elix, Inc), Shinya Yuki (Elix, Inc)

CUBC: A Generalized Representation Learning Method for User Behavioral Sequence - [Poster] [Zoom] - Yongqing Wang (Institute of Computing Technology, Chinese Academy of Sciences), Haopeng Zhang (Institute of Computing Technology, Chinese Academy of Sciences), Hao Gu (Tencent Technology (SZ) Co., Ltd.), Lingling Yi (Tencent Technology (SZ) Co., Ltd.), Huawei Shen (Institute of Computing Technology, Chinese Academy of Sciences), Xueqi Cheng (Institute of Computing Technology, Chinese Academy of Sciences)

Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study - [Poster] [Zoom] - Ananya Karthik (Stanford University), Mike H Wu (Stanford University), Noah Goodman (Stanford University), Alex Tamkin (Stanford University)

CORE: Self- and Semi-supervised Tabular Learning with COnditional REgularizations - [Poster] [Zoom] - Xintian Han (New York University), Rajesh Ranganath (New York University)

Do Self-Supervised and Supervised Methods Learn Similar Visual Representations? - [Poster] [Zoom] - Tom G Grigg (Apple), Dan Busbridge (Apple), Jason Ramapuram (Apple Inc), Russ Webb (Apple)

Self-supervised Test-time Adaptation on Video Data - [Poster] [Zoom] - Fatemeh Azimi (TU Kaiserslautern), Sebastian Palacio (DFKI), Federico Raue (DFKI), Jörn Hees (DFKI), Luca Bertinetto (University of Oxford), Andreas Dengel (DFKI GmbH)

Distribution Estimation to Automate Transformation Policies for Self-Supervision - [Poster] [Zoom] - Seunghan Yang (Qualcomm AI Research), Debasmit Das (Qualcomm), Simyung Chang (Qualcomm Korea YH), Sungrack Yun (Qualcomm AI Research), Fatih Porikli (Qualcomm AI Research)

Branching Out for Better BYOL - [Poster] [Zoom] - Azad Singh (Indian Institute of Technology Jodhpur), Deepak Mishra (IIT Jodhpur)

Contrastive Representation Learning with Trainable Augmentation Channel - [Poster] [Zoom] - Masanori Koyama (Preferred Networks Inc.), Kentaro Minami (Preferred Networks, Inc.), Takeru Miyato (Preferred Networks, Inc.), Yarin Gal (University of Oxford)

Finding Useful Predictions by Meta-gradient Descent to Improve Decision-making - [Poster] [Zoom] - Alex K Kearney (University of Alberta), Johannes Guenther (University of Alberta), Anna Koop (University of Alberta), Patrick M. Pilarski (University of Alberta)