Representation Learning

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Overview

Representation learning studies how to turn raw data—such as images, text, graphs, time series, and sensor signals—into features that make important patterns easier to use. Common approaches include contrastive and masked prediction, autoencoders, predictive latent models, sparse coding, and methods that impose structure such as geometry, hierarchy, or disentangled subspaces. Good representations can reduce label needs and support transfer across tasks, domains, and modalities, with evaluations spanning linear probes, retrieval, classification, and downstream control. A central tension is that compression and invariance can discard information a later task needs, so no single objective or representation geometry works best across settings.

Latest papers

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798 papers130 papers in the last four weeks, up 97% on the four weeks before. 0.8% of all new papers.

  1. From Pixels to Patches: Pooling Strategies for Earth Embeddings

    Mar 2, 2026Isaac Corley, Caleb Robinson, Inbal Becker-Reshef +1Spatial GeneralizationGeospatial Foundation Models

    Wherobots · Microsoft AI for Good Research Lab

  2. Never Saddle for Reparameterized Steepest Descent as Mirror Flow

    Mar 2, 2026Tom Jacobs, Chao Zhou, Rebekka BurkholzNeural Network Training DynamicsGradient Descent Dynamics

    CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

  3. Spectral Overfitting in Noisy Linear Probing of Pretrained Representations

    Mar 2, 2026Zice Wang, Zhenyu ZhangRepresentation LearningLinear Probing

    School of Future Technology, Northeastern University, Shenyang, China.

  4. Discrete World Models via Regularization

    Mar 2, 2026Davide Bizzaro, Luciano SerafiniWorld Model LearningWorld Models

    University of Padua, Padua, Italy · Fonadazione Bruno Kessler, Trento, Italy

  5. Asking the Right Questions: Ontology-Grounded Interpretable Embeddings for Biomedical Text

    Mar 2, 2026Yixuan Tang, Zhenghong Lin, Yandong Sun +3Text EmbeddingsRepresentation Learning

    School of Computing, National University of Singapore

  6. ResGene-T: A Tensor-Based Residual Network Approach for Genomic Prediction

    Feb 28, 2026Kuldeep Pathak, Kapil Ahuja, Eric de SturlerGenomicsConvolutional Neural Networks

    Department of Computer Science and Engineering, Indian Institute of Technology Indore, India 453552 · Department of Mathematics, Virginia Tech, Blacksburg, USA 24061

  7. Social-JEPA: Emergent Geometric Isomorphism

    Feb 28, 2026Youjin WangRepresentation LearningVisual Representation Learning

    Renmin University of China

  8. InfoNCE Induces Gaussian Distribution

    Feb 27, 2026Roy Betser, Eyal Gofer, Meir Yossef Levi +1Contrastive LearningRepresentation Learning

    Technion - Israel Institute of Technology

  9. SegReg: Latent Space Regularization for Improved Medical Image Segmentation

    Feb 26, 2026Puru Vaish, Amin Ranem, Felix Meister +3Latent-Space RegularizationMedical Image Segmentation

    Department of Applied Mathematics, Technical Medical Centre, University of Twente, The Netherlands · Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany

  10. Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks

    Feb 26, 2026Katerina Papagiannouli, Dario Trevisan, Giuseppe Pio ZittoLarge Deviation PrinciplesBayesian Neural Networks

    Dipartimento di Matematica, Università di Pisa

  11. Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning

    Feb 26, 2026Taishu Arashima, Hiroshi Kera, Kazuhiko KawamotoHuman Trajectory PredictionTrajectory Prediction

    Chiba University

  12. DexRepNet++: Learning Dexterous Robotic Manipulation with Geometric and Spatial Hand-Object Representations

    Feb 25, 2026Qingtao Liu, Zhengnan Sun, Yu Cui +5Dexterous ManipulationRepresentation Learning

    the College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · the College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology, Zhejiang University, China · the Department of Computer Science, National University of Singapore, 15 Computing Dr, 117418, Singapore

  13. CG-DMER: Hybrid Contrastive-Generative Framework for Disentangled Multimodal ECG Representation Learning

    Feb 24, 2026Ziwei Niu, Hao Sun, Shujun Bian +4Electrocardiogram Representation LearningMultimodal Disentangled Representation Learning

    Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · College of information science and engineering, Ritsumeikan university, ibaraki Osaka, Japan +1

  14. A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

    Feb 23, 2026Nicolas Anguita, Francesco Locatello, Andrew M. Saxe +4Neural Network GeneralizationRepresentation Learning

    Department of Engineering, University of Cambridge · Institute of Science and Technology, Austria (ISTA) · Gatsby Computational Neuroscience Unit and Sainsbury Wellcome Centre, UCL +1

  15. Cross-lingual Matryoshka Representation Learning across Speech and Text

    Feb 23, 2026Yaya Sy, Dioula Doucouré, Christophe Cerisara +1Representation LearningAudio-Text Retrieval

    LORIA, CNRS · Soynade Research

  16. PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics

    Feb 23, 2026Kihyuk Yoon, Lingchao Mao, Catherine Chong +3Longitudinal EHR ModelingRepresentation Learning

    Georgia Institute of Technology · Meta · Mayo Clinic

  17. Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

    Feb 22, 2026Ali Saheb Pasand, Johan Obando-Ceron, Aaron Courville +2RL StabilityRepresentation Learning for RL

    Mila – Quebec AI Institute · McGill · Université de Montréal +1

  18. LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning

    Feb 22, 2026Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1Spatio-Temporal AttentionWireless Communications

  19. INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection

    Feb 22, 2026Rahul D RayEncrypted Network Traffic AnalysisAnomaly Detection

    Department of Electronics & Electrical Engineering, BITS Pilani, Hyderabad Campus

  20. Position: Evaluation of ECG Representations Must Be Fixed

    Feb 19, 2026Zachary Berger, Daniel Prakah-Asante, John Guttag +1Electrocardiogram Representation LearningRepresentation Learning

    Massachusetts Institute of Technology, Cambridge, MA, USA · Massachusetts General Hospital, Boston, MA, USA

  21. Unified Latents (UL): How to train your latents

    Feb 19, 2026Jonathan Heek, Emiel Hoogeboom, Thomas Mensink +1Diffusion AutoencoderLatent Diffusion Models

    Google DeepMind Amsterdam

  22. Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems

    Feb 19, 2026Alexander Klemps, Denis Ilia, Pradeep Kr. Banerjee +3Representation LearningGenerative Modeling

    Institute for Data Science Foundations, Hamburg University of Technology, Germany · Deutsches Elektron-Synchrotron (DESY), Hamburg, Germany

  23. IntentCUA: Learning Intent-level Representations for Skill Abstraction and Multi-Agent Planning in Computer-Use Agents

    Feb 19, 2026Seoyoung Lee, Seobin Yoon, Seongbeen Lee +4Computer-Use AgentsLong-Horizon Agent Tasks

    Sookmyung Women’s University, Seoul, Republic of Korea

  24. Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts

    Feb 18, 2026Sakib Ahammed, Xia Cui, Wenqi Lu +4Representation LearningVisual Representation Learning

    Department of Computing and Mathematics, Manchester Metropolitan University The Dalton Building, Chester Street, M1 5GD Manchester

  25. Knowledge-Embedded Latent Projection for Robust Representation Learning

    Feb 18, 2026Weijing Tang, Ming Yuan, Zongqi Xia +1Representation LearningLatent Variable Models

    Department of Statistics and Data Science, Carnegie Mellon University · Department of Statistics, Columbia University · Department of Neurology, University of Pittsburgh +1

  26. On the Power of Source Screening for Learning Shared Feature Extractors

    Feb 18, 2026Leo Muxing Wang, Connor Mclaughlin, Lili SuShared Representation LearningRepresentation Learning

    Northeastern University

  27. A unified theory of feature learning in RNNs and DNNs

    Feb 17, 2026Jan P. Bauer, Kirsten Fischer, Moritz Helias +1Recurrent Neural NetworksRepresentation Learning

    Gatsby Computational Neuroscience Unit, UCL, London, UK · IAS-6, Forschungszentrum Juelich, Juelich, Germany

  28. Logit Distance Bounds Representational Similarity

    Feb 17, 2026Beatrix M. G. Nielsen, Emanuele Marconato, Luigi Gresele +2Representational Similarity AnalysisRepresentation Learning

    IT University of Copenhagen, Denmark · University of Trento, Italy · University of Copenhagen, Denmark +2

  29. Symmetry in language statistics shapes the geometry of model representations

    Feb 16, 2026Dhruva Karkada, Daniel J. Korchinski, Andres Nava +2Representation LearningNeural Representation Geometry

    1UC Berkeley · 2EPFL · 3Johns Hopkins +1

  30. Query as Anchor: Scenario-Adaptive User Representation via Large Language Model

    Feb 16, 2026Jiahao Yuan, Yike Xu, Jinyong Wen +9User ModelingRepresentation Learning

    Ant Group · East China Normal University · Zhejiang University