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

804 papers133 papers in the last four weeks, up 102% on the four weeks before. 0.8% of all new papers.

  1. A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution

    Aug 16, 2026Jie Wei, Yue Liu, Xiaochuan Tang +3Dynamic Graph LearningTime Series Forecasting

    Chengdu University of Technology, Chengdu, 610000, China. · Chengdu Neusoft University, Chengdu, 610000, China. · Jilin University, Changchun, 130000, China.

  2. SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences

    Aug 15, 2026Tsz Fung Pang, Po Jen Chen, Nimish Ronghe +2User ModelingSequential Recommendation

    PayPal AI San Jose, CA, USA

  3. Invariant Pretraining for Robust Code Representations

    Aug 15, 2026Yifeng He, Yundi Xu, Christopher Castro Gaw Gonzalo +2Representation LearningInvariant Representation Learning

    University of California at Davis, Davis, USA · The University of Hong Kong, China

  4. Learning reshapes power-law anisotropy in internal representations

    Aug 15, 2026Asahi Nakamuta, Jun-nosuke TeramaeNeural Representation GeometryRepresentation Learning

    Graduate School of Informatics, Kyoto University

  5. RecipeNet: A Hierarchical Transformer for Recipe Data

    Aug 14, 2026Pin-Yen Huang, Sachin Chhabra, Prasanth Sai Gouripeddi +2Representation LearningHierarchical Representation Learning

    Arizona State University Tempe, Arizona, USA · Applied Materials Santa Clara, California, USA · University of Illinois Chicago Chicago, Illinois, USA

  6. CytoBERT: A Foundation Model for Cytometry Data

    Aug 14, 2026Syed Abdul Haseeb Qadri, Bjarne C. Hiller, Felix Blanke +7Representation LearningTransfer Learning

    University of Rostock, Rostock, Germany · Marburg University, Marburg, Germany · Hessian Center for Artificial Intelligence, Darmstadt, Germany

  7. Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features

    Aug 14, 2026Yao Guo, Ligen Shi, Shuchen Sun +2Low-Rank Representation LearningNeural Rendering

    Institute of Computational Imaging, Beijing Information Science and Technology University, Beijing 102206, China · College of Computer Science (College of Software), Inner Mongolia University, Hohhot 010021, China

  8. Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity

    Aug 13, 2026Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini +5Fall DetectionRepresentation Learning

    School of Computer Science, University College Dublin (UCD), Ireland · Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Italy · Department of Clinical Gerontology, Robert Bosch Hospital, Stuttgart, Germany +2

  9. Dual-Manifold Geometry Guided Representation Learning: Adaptive Coupling between Kernel and Data Spaces

    Aug 13, 2026Wencong Zhang, Yue Zhang, Meiyan Huang +2Representation LearningNeural Representation Geometry

    School of Biomedical Engineering, Southern Medical University, China, Guangzhou 510515

  10. Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization

    Aug 13, 2026Fin Amin, Sounak Dutta, Paul D. FranzonDeep Kernel LearningRepresentation Learning

    Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

  11. Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification

    Aug 12, 2026Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-AkbariRepresentation LearningHyperspectral Image Classification

    School of Built Environment, Engineering, and Computing, Leeds Beckett University. Leeds LS6 3QS, United Kingdom.

  12. Instruction Alignment for Binary Code Representation Learning

    Aug 12, 2026Huaijin Wang, Shuai WangSoftware EngineeringRepresentation Learning

    Shandong University · Hong Kong University of Science and Technology

  13. Columnar-Embedder: A Biologically Inspired Cortical Architecture for Binary Sparse Distributed Graph Representations

    Aug 12, 2026Mohamed Abidalrekab, Dan HammerstromGraph EmbeddingRepresentation Learning

    Portland State University, Electrical and Computer Engineering, Portland, 97223, USA

  14. Beyond a Bag of Features: Set-Level Instability in Sparse Autoencoders

    Aug 11, 2026Nikolai Bolik, Lennart Stöpler, Artur AndrzejakRepresentation LearningLLM Interpretability

    Heidelberg University

  15. VIScore: Diagnosing Planning-Relevant Quality in Latent World Models

    Aug 11, 2026Haiyu Wu, Randall Balestriero, Morgan LevineRepresentation LearningWorld Models

    Altos Labs · Brown University

  16. SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

    Aug 11, 2026Moti Rattan Gupta, Anupam SobtiRepresentation LearningAgricultural Remote Sensing

    Plaksha University, Mohali, Punjab, India

  17. Multiple Scale Latents for Learned Image Compression

    Aug 11, 2026Jonas Brenig, Radu TimofteLearned Image CompressionRepresentation Learning

    Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany

  18. FiGuRO: Intrinsic Dimension Estimation for Multi-Modal Data

    Aug 11, 2026Viktoria Schuster, Sana Tonekaboni, Caroline UhlerIntrinsic DimensionalityRepresentation Learning

    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA · Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA · Technical University of Denmark, Lyngby, Denmark +1

  19. A Joint-Distribution Route to Fair Representations with Continuous Sensitive Attributes

    Aug 11, 2026Yijin Ni, Xiaoming HuoRepresentation LearningAlgorithmic Fairness

    H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205 USA

  20. ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models

    Aug 10, 2026Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji +1Representation LearningTemporal Representation Learning

    Institut universitaire de France

  21. Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection

    Aug 10, 2026Stefanos Gkikas, Thomas Kassiotis, Yang Guo +5Representation LearningVisual Representation Learning

    Honda Research Institute Japan Wako City, Japan · Department of Electronic Engineering Hellenic Mediterranean University Chania, Greece · Faculty of Information Science and Engineering Ocean University of China Qingdao, China +2

  22. Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production

    Aug 10, 2026Xiao Liu, Shiwei Gan, Yafeng Yin +5Sign Language TranslationRepresentation Learning

    State Key Laboratory of Novel Software Technology, Nanjing University, Suzhou, Jiangsu, China · State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China

  23. Residual Algebra for Representation-Preserving Learning

    Aug 7, 2026Yao WuRepresentation LearningQuantitative Finance

  24. JUMP-lite: Compact, reproducible benchmarking of cell representations

    Aug 7, 2026Alán F. Muñoz, Johan Fredin Haslum, Runxi Shen +2Representation LearningMicroscopy Image Analysis

    Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

  25. Cascading Through the Hierarchy: Regularizer-Induced Feature Detection as Phase Transitions in Deep Linear Neural Networks

    Aug 6, 2026Björn Ladewig, Ibrahim Talha Ersoy, Karoline WiesnerStatistical Physics of LearningRepresentation Learning

    Institute of Physics and Astronomy, University of Potsdam

  26. RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction

    Aug 6, 2026Yiting Zheng, Cheng Fang, Anthony Donofrio +1Representation LearningGraph Representation Learning

    Discovery Chemistry, Merck & Co., Inc. Boston, MA, USA · Discovery Chemistry, Merck & Co., Inc. Cambridge, MA, USA

  27. How Far Do Simple Transformations Translate Across Text Embedding Models?

    Aug 6, 2026Sid Ali Hamideche, Louis-Adrien Dufrène, Quentin Lampin +1Representation LearningText Embedding Evaluation

    Orange Research · Orange

  28. PhyLatent: Learning Dynamics-Relevant Representations for JEPA World Models

    Aug 6, 2026Xi Zeng, Haojie Ren, Ziying Song +2Representation LearningWorld Model Learning

    School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · The University of Sheffield, Sheffield, United Kingdom · School of Artificial Intelligence (School of Software), Yanshan University, Qinhuangdao, China

  29. Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning

    Aug 5, 2026Xuehang Guo, Pengyuan Li, Tom Hope +3Multi-View ConsistencyRepresentation Learning

    William & Mary · IBM · Allen Institute for AI +2

  30. Robustness Emerges Early in Training Dynamics, but Is Not Preserved

    Aug 5, 2026Jiangang Yang, Wenhui Shi, Lu Hu +2Distribution Shift RobustnessRepresentation Learning

    Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China