Representation Learning

Ask about this topic

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

CardsList

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

  1. Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance

    Apr 17, 2026Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane +2Disentangled Representation LearningRepresentation Learning

    Intelligent Transportation Systems Research Team, Cerema, 8 rue Bernard Palissy, 63017 Clermont-Ferrand, France · Intelligent Transportation Systems Research Team, Cerema, 1 avenue du Colonel Roche, 31400 Toulouse, France · Clermont Auvergne INP, Universit´e Clermont Auvergne, Institut Pascal, CNRS, 4 Avenue Blaise Pascal, 63178 Clermont-Ferrand, France

  2. SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification

    Apr 17, 2026Alexander Musiat, Nikolas Ebert, Oliver WasenmüllerRepresentation LearningHyperspectral Imaging

    Mannheim University of Applied Sciences, Germany

  3. Intermediate Layers Encode Optimal Biological Representations in Single-Cell Foundation Models

    Apr 16, 2026Vincenzo Yuto Civale, Roberto Semeraro, Andrew David Bagdanov +1Representation LearningTrajectory Inference

    University of Siena · University of di Firenze · University of Firenze

  4. Learning to Draw ASCII Improves Spatial Reasoning in Language Models

    Apr 16, 2026Shiyuan Huang, Li Liu, Jincheng He +1Representation LearningSpatial Reasoning

    University of California, Santa Cruz

  5. Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation

    Apr 14, 2026Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Chen Chen +2Representation LearningBiomedical KGs

    University of Kansas, USA

  6. Spectral Entropy Collapse as a Phase Transition in Delayed Generalisation: An Interventional and Predictive Framework for Grokkin

    Apr 13, 2026Truong Xuan Khanh, Truong Quynh Hoa, Luu Duc Trung +1GrokkingNeural Network Training Dynamics

    H&K Research Studio, Clevix LLC, Hanoi, Vietnam · Banking Academy of Vietnam, Hanoi, Vietnam

  7. Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

    Apr 13, 2026Zhenghao Xie, Jing Xiao, Zhenqi Wang +4Remote Sensing Image UnderstandingCost-Aware Inference

    School of Artificial Intelligence, Wuhan University, Wuhan, China · Hangzhou Institute of Technology, Xidian University, Hangzhou, China · Hubei Luojia Laboratory, Wuhan University, Wuhan, China

  8. CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy

    Apr 13, 2026Zehao Qin, Xiaojian Lin, Ping Zhang +6Electrocardiogram Representation LearningRepresentation Learning

    Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, PR China · Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, PR China · Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, PR China +3

  9. ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning

    Apr 12, 2026Kewei Zhu, Cameron Wilson, Bartosz Mazur +3Molecular Representation LearningMaterials Science

    Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom · Department of Micro, Nano, and Bioprocess Engineering, Faculty of Chemistry, Wroclaw University of Science and Technology, Wroclaw 50-370, Poland · Manufacturing Futures Lab, University College London, 7 Sidings Street, London, E20 2AE, United Kingdom

  10. NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning

    Apr 12, 2026Yanyi Su, Hongshuai Wang, Zhifeng Gao +1Representation LearningMolecular Representation Learning

    State Key Laboratory of Physical Chemistry of Solid Surface, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China · DP Technology · Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory (IKKEM), Xiamen, China +1

  11. Multi-modal, multi-scale representation learning for satellite imagery analysis just needs a good ALiBi

    Apr 11, 2026Patrick Kage, Pavlos AndreadisRepresentation LearningRemote Sensing Image Understanding

    Artificial Intelligence and its Applications Institute, (University of Edinburgh, 10 Crichton Street, Newington, Edinburgh EH8 9AB)

  12. DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation

    Apr 10, 2026Li Huang, Zhongxin Liu, Yifan Wu +6Secure Code GenerationLLM Code Generation

    Chongqing University · The State Key Laboratory of Blockchain and Data Security, Zhejiang University · Peking University

  13. A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need

    Apr 9, 2026Hananel Hazan, Yanbo Zhang, Benedikt Hartl +1Random Feature MethodsParameter-Efficient Fine-Tuning

    Allen Discovery Center at Tufts University, Medford, Massachusetts, USA · Institute for Theoretical Physics, TU Wien, Wien, Austria · Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA

  14. The Impact of Dimensionality on the Stability of Node Embeddings

    Apr 9, 2026Tobias Schumacher, Simon Reichelt, Markus StrohmaierRepresentation LearningAlgorithmic Stability

    University of Mannheim · RWTH Aachen University · GESIS - Leibniz Institute for the Social Sciences +1

  15. Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction

    Apr 9, 2026Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea +1Temporal Representation LearningRobotics

    DIBRIS Department, University of Genoa, Via All’Opera Pia 13 16145 Genoa, Italy · CONTACT Unit, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genoa, Italy · International Research Center for Neurointelligence, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

  16. MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

    Apr 9, 2026Junyao Gao, Sibo Liu, Jiaxing Li +6Image Style TransferRepresentation Learning

    Tongji Univeristy · Tencent · Nanyang Technological University +4

  17. Multimodal Latent Reasoning via Predictive Embeddings

    Apr 9, 2026Ashutosh Adhikari, Mirella LapataVLM ReasoningMultimodal Reasoning

    School of Informatics University of Edimburgh

  18. Needle in a Haystack: One-Class Representation Learning for Detecting Rare Malignant Cells in Computational Cytology

    Apr 9, 2026Swarnadip Chatterjee, Vladimir Basic, Arrigo Capitanio +2Whole-Slide Image AnalysisComputational Pathology

    Department of Information Technology, Uppsala University, Sweden · Department of Clinical Diagnostics, School of Health Sciences, Jönköping University, Sweden · Department of Pathology, Linköping University Hospital, Sweden

  19. Bi-Lipschitz Autoencoder With Injectivity Guarantee

    Apr 8, 2026Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +2AutoencodersRepresentation Learning

    University of Pennsylvania

  20. Variational Feature Compression for Model-Specific Representations

    Apr 8, 2026Zinan Guo, Zihan Wang, Chuan Yan +3Privacy-Preserving Representation LearningPrivacy-Preserving ML

    The University of Queensland, Brisbane, Australia · University of Wollongong, Wollongong, Australia · City University of Hong Kong, Hong Kong SAR, China

  21. Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks

    Apr 7, 2026Guillaume Corlouer, Avi Semler, Alexander Strang +1Gradient Descent DynamicsStochastic Gradient Descent

    Moirai · University of Oxford · University of California, Berkeley +1

  22. Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective

    Apr 7, 2026Jonas Muth, Zdravko Marinov, Simon ReißRepresentation LearningMedical Imaging

    Karlsruhe Institute of Technology, Karlsruhe, Germany

  23. Identity-Aware U-Net: Fine-grained Cell Segmentation via Identity-Aware Representation Learning

    Apr 7, 2026Rui XiaoInstance SegmentationU-Net

    South China University of Technology

  24. Hierarchical Contrastive Learning for Multimodal Data

    Apr 7, 2026Huichao Li, Junhan Yu, Doudou ZhouRepresentation LearningMultimodal Contrastive Learning

    Department of Mathematical Sciences, University of Chinese Academy of Sciences · Department of Statistics and Data Science, National University of Singapore

  25. Training Without Orthogonalization, Inference With SVD: A Gradient Analysis of Rotation Representations

    Apr 7, 2026Chris Choy3D Representation LearningRepresentation Learning

    NVIDIA

  26. IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation

    Apr 6, 2026Anjali Kantharuban, Aarohi Srivastava, Fahim Faisal +5Representation Learning

    Carnegie Mellon University · University of Notre Dame · George Mason University +2

  27. The Indra Representation Hypothesis for Multimodal Alignment

    Apr 6, 2026Jianglin Lu, Hailing Wang, Kuo Yang +3Representation LearningCross-Modal Alignment

    Department of Electrical and Computer Engineering, Northeastern University · Adobe Research · Khoury College of Computer Science, Northeastern University

  28. Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning

    Apr 6, 2026Ryuki Tezuka, Chihiro Nakatani, Norimichi UkitaRepresentation LearningHuman Activity Recognition

    Toyota Technological Institute

  29. ArrowFlow: Hierarchical Machine Learning in the Space of Permutations

    Apr 5, 2026Ozgur YilmazRepresentation LearningHierarchical Representation Learning

    Department of Artificial Intelligence Adana Science and Technology University, Adana, Turkey

  30. Geometric Limits of Knowledge Distillation: A Minimum-Width Theorem via Superposition Theory

    Apr 5, 2026Nilesh Sarkar, Dawar Jyoti DekaKnowledge DistillationNeural Representation Geometry

    Erd˝os AI Labs