| 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 |