ID | Paper |
5 | [Oral] Bridging State and History Representations: Understanding Self-Predictive RL Tianwei Ni, Benjamin Eysenbach, Erfan SeyedSalehi, Michel Ma, Clement Gehring, Aditya Mahajan, Pierre-Luc Bacon |
24 | [Oral] Learning Orthonormal Features in Self-Supervised Learning using Functional Maximal Correlation Bo Hu, Yuheng Bu, Jose Principe |
29 | [Oral] Learning to Embed Time Series Patches Independently Seunghan Lee, Taeyoung Park, Kibok Lee |
52 | [Oral] On the Varied Faces of Overparameterization in Supervised and Self-Supervised Learning Matteo Gamba, Arna Ghosh, Kumar Krishna Agrawal, Blake Aaron Richards, Hossein Azizpour, Mårten Björkman |
1 | Benchmarking self-supervised video representation learning Akash Kumar, Ashlesha Kumar, Vibhav Vineet, Yogesh Rawat |
2 | Adversarial perturbation based latent reconstruction for domain-agnostic self-supervised learning Kuilin Chen, Sijie Tian, Chi-Guhn Lee |
4 | Exploring Target Representations for Masked Autoencoders xingbin liu, Jinghao Zhou, Tao Kong |
6 | Augmentation-aware Self-Supervised Learning with Conditioned Projector Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński, Bartłomiej Twardowski, Jacek Tabor, Marek Śmieja |
7 | Does Unconstrained Unlabeled Data Help Semi-Supervised Learning? Shuvendu Roy, Ali Etemad |
8 | SurgMAE: Masked Autoencoders for Long Surgical Video Analysis Muhammad Abdullah Jamal, Omid Mohareri |
9 | The Triad of Failure Modes and a Possible Way Out Emanuele Sansone |
10 | An Information-Theoretic Understanding of Maximum Manifold Capacity Representations Berivan Isik, Rylan Schaeffer, Victor Lecomte, Mikail Khona, Yann LeCun, Sanmi Koyejo, Andrey Gromov, Ravid Shwartz-Ziv |
11 | Simple Contrastive Representation Learning for Time Series Forecasting Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, Michael Krauthammer |
12 | Language Model Training Paradigms for Clinical Feature Embeddings Yurong Hu, Manuel Burger, Gunnar Ratsch, Rita Kuznetsova |
13 | Self-Distilled Representation Learning for Time Series Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach |
14 | MOFO: MOtion FOcused Self-Supervision for Video Understanding Mona Ahmadian, Frank Guerin, Andrew Gilbert |
15 | Scaling may be all you need for achieving human-level object recognition with human-like visual experience Emin Orhan |
16 | Self-Supervised Learning Meets Liver Ultrasound Imaging Abder-Rahman Ali, Anthony Samir |
17 | No Free Lunch in Self Supervised Representation Learning Ihab Bendidi, Adrien Bardes, Cohen Ethan, Alexis Lamiable, Guillaume Bollot, Auguste Genovesio |
18 | Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models Shentong Mo, Zhun Sun, Chao Li |
19 | Augmentation matters: Representation learning for Strong Gravitational lensing Kuan-Wei Huang, Po-Wen Chang, Joshua Fagin, James Chan, Joshua Yao-Yu Lin |
20 | Iterated Piecewise Affine (IPA) Approximation for Language Modeling Davood Shamsi, Wen-yu Hua, Brian Williams |
21 | DAPO: Self-Supervised Domain Adaptation for 6DoF Pose Estimation juseong jin, Eunju Jeong, Joonmyun Cho, Juni PARK, Young-Gon Kim |
22 | Soft Contrastive Learning for Time Series Seunghan Lee, Taeyoung Park, Kibok Lee |
23 | Visualizing the loss landscape of Self-supervised Vision Transformer Youngwan Lee, Jeffrey Willette, Jonghee Kim, Sung Ju Hwang |
26 | HyperMAE: Modulating Implicit Neural Representations for MAE Training Varun Belagali, Lei Zhou, Xiang Li, Dimitris Samaras |
27 | Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda, Narges Razavian |
28 | Ring Attention with Blockwise Transformers for Near-Infinite Context Hao Liu, Matei Zaharia, Pieter Abbeel |
30 | Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation Kangwook Jang, Sungnyun Kim, Se-Young Yun, Hoi-Rin Kim |
31 | Neurosymbolic Grounding for Compositional Generalization Atharva Sehgal, Arya Grayeli, Jennifer Sun, Swarat Chaudhuri |
32 | Adaptive Resolution Loss: An Efficient and Effective Loss for Time Series Hierarchical Contrastive Self-Supervised Learning Framework Kevin Garcia, Juan Manuel Perez Jr, Yifeng Gao |
33 | Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations Cian Eastwood, Julius von Kügelgen, Linus Ericsson, Diane Bouchacourt, Pascal Vincent, Bernhard Schölkopf, Mark Ibrahim |
34 | On Improving the Sample Efficiency of Non-Contrastive SSL Kumar Agrawal, Arna Ghosh, Adam Oberman, Blake Richards |
35 | Unsupervised Segmentation of Colonoscopy Images Heming Yao, Jérôme Lüscher, Benjamin Gutierrez Becker, Josep Arús-Pous, Tommaso Biancalani, Amelie Bigorgne, David Richmond |
36 | WERank: Rank Degradation Prevention for Self-Supervised Learning via Weight Regularization Ali Pasand, Reza Moravej, Mahdi Biparva, Ali Ghodsi |
37 | BarcodeBERT: Transformers for Biodiversity Analysis Pablo Millan Arias, Niousha Sadjadi, Monireh Safari, ZeMing Gong, Austin Wang, Scott Lowe, Joakim Haurum, Iuliia Zarubiieva, Dirk Steinke, Lila Kari, Angel Chang, Graham Taylor |
39 | Self-supervised Learning for User Sequence Modeling Yuhan Liu, Lin Ning, Neo Wu, Karan Singhal, Philip Andrew Mansfield, Devora Berlowitz, Bradley Green |
40 | Non-Vacuous Generalization Bounds for Large Language Models Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim Rudner, Micah Goldblum, Andrew Wilson |
41 | Evolving Graph Generalization Estimation via Self-Supervised Learning Bin Lu, Tingyan Ma, Xiaoying Gan, Luoyi Fu, Xinbing Wang, Chenghu Zhou, Shiyu Liang |
42 | SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling Benjamin Missaoui, Chongbin Yuan |
43 | Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning. Adrian Atienza, Jakob E. Bardram, Sadasivan Puthusserypady |
44 | A Simple Framework for Self-Supervised Learning of Sample-Efficient World Models Jan Robine, Marc Höftmann, Stefan Harmeling |
45 | Leveraging Uniformity of Normalized Embeddings for Sequential Recommendation Hyunsoo Chung, Jungtaek Kim |
46 | MeSa: Masked, Geometric, and Supervised Pre-training for Monocular Depth Estimation Muhammad Osama Khan, Junbang Liang, Chun-Kai Wang, Shan Yang, Yu Lou |
47 | Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning Sharut Gupta, Joshua Robinson, Derek Lim, Soledad Villar, Stefanie Jegelka |
48 | Improving Domain Generalization in Contrastive Learning Using Adaptive Temperature Control Katie Matton, Robert A Lewis, Rosalind Picard, John Guttag |
50 | Multimodal Distillation of CLIP Models Georgios Smyrnis, Sriram Ravula, sujay sanghavi, Alex Dimakis |
51 | LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures Vimal Thilak, Chen Huang, Omid Saremi, Laurent Dinh, Hanlin Goh, Preetum Nakkiran, Joshua M. Susskind, Etai Littwin |
53 | Bootstrap Your Own Variance Polina Turishcheva, Jason Ramapuram, Sinead Williamson, Dan Busbridge, Eeshan Gunesh Dhekane, Russell Webb |
54 | Online Feature Updates Improve Online (Generalized) Label Shift Adaptation Ruihan Wu, Siddhartha Datta, Yi Su, Dheeraj Baby, Yu-Xiang Wang, Kilian Q Weinberger |
55 | Enhancing CLIP with a Third Modality Efthymios Tsaprazlis, Georgios Smyrnis, Alex Dimakis, Petros Maragos |
56 | Self-Supervised Pretraining for Improved Downstream Decoding of Audio-Evoked fMRI Sequences Sean Paulsen, Michael Casey |
58 | Spectral Temporal Contrastive Learning Sacha Morin, Somjit Nath, Samira Ebrahimi Kahou, Guy Wolf |
59 | Self-supervised Representation Learning from Random Data Projectors Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs |
60 | Perceptual Group Tokenizer: Building Perception with Iterative Grouping Zhiwei Deng, Ting Chen, Yang Li |
61 | Posterior Sampling on Simsiam: Rethinking Optimization in Siamese Self-Supervised Learning Daniel De Mello, Ruqi Zhang, Bruno Ribeiro |
62 | MolSiam: Simple Siamese Self-supervised Representation Learning for Small Molecules Joshua Yao-Yu Lin, Michael Maser, Nathan C. Frey, Gabriele Scalia, Omar Mahmood, Pedro O. Pinheiro, Ji Won Park, Stephen Ra, Andrew Martin Watkins, Kyunghyun Cho |
63 | Self-Supervised Image Captioning with CLIP Chuanyang Jin |
64 | Generalization properties of contrastive world models Kandan Ramakrishnan, R. James Cotton, Xaq Pitkow, Andreas S. Tolias |
65 | CCA with Shared Weights for Self-Supervised Learning James Chapman, Lennie Wells |
66 | Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery Sarah Rastegar, Hazel Doughty, Cees G. M. Snoek |
68 | No Representation Rules Them All in Category Discovery Sagar Vaze, Andrea Vedaldi, Andrew Zisserman |
69 | Generalized Category Discovery with Hierarchical Label Smoothing Sarah Rastegar, Yuki M Asano, Hazel Doughty, Cees G. M. Snoek |
70 | Zero-shot Clustering of Embeddings with Self-Supervised Learnt Encoders Scott C Lowe, Joakim Bruslund Haurum, Sageev Oore, Thomas B. Moeslund, Graham W. Taylor |
71 | FroSSL: Frobenius Norm Minimization for Self-Supervised Learning Oscar Skean, Aayush Dhakal, Nathan Jacobs, Luis Gonzalo Sanchez Giraldo |
72 | Multi-Task Learning with Self-Supervised Objectives can Improve Worst-Group Outcomes Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig |
74 | How does semi-supervised learning with pseudo-labelers work? A case study Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu |
75 | Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation Paul Engstler, Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina |
76 | Application of Self Supervised Vision Transformers for Multiplexed Microscopy Images and Its Challenges Gantugs Atarsaikhan, Isabel Mogollon, Katja Välimäki, Teijo Pellinen, Tuomas Mirtti, Lassi Paavolainen |
77 | Learning Useful Representations of Recurrent Neural Network Weight Matrices Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber |
78 | Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks Jannik Sheikh, Andrew Melnik, Gora Chand Nandi, Robert Haschke |
79 | Can semi-supervised learning use all the data effectively? A lower bound perspective Alexandru Tifrea, Gizem Yüce, Amartya Sanyal, Fanny Yang |
80 | Identifiable attribution maps using regularized contrastive learning Steffen Schneider, Rodrigo González Laiz, Markus Frey, Mackenzie W Mathis |