Representation Learning

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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.

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

Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations
May 12, 2026Souptik Sen, Raneen Younis, Zahra AhmadiMultimodal Learning Representation Learning
Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Germany · Lower Saxony Center for AI and Causal Methods in Medicine (CAIMed), Hannover, Germany
Attention-Based Multimodal Survival Prediction with Cross-Modal Bilinear Fusion
May 12, 2026Hassan Keshvarikhojasteh, Josien P. W. Pluim, Mitko VetaRepresentation Learning Cancer Survival Prediction
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
May 12, 2026SeongMin Jin, Doo Seok JeongRepresentation Learning Visual Representation Learning
1Division of Materials Science and Engineering · Department of Semiconductor Engineering
Debiased Model-based Representations for Sample-efficient Continuous Control
May 12, 2026Jiafei Lyu, Zichuan Lin, Scott Fujimoto +5Representation Learning Continuous Control
1Tencent Hunyuan · 2McGill University · School of Computer
Conditional Memory Enhanced Item Representation for Generative Recommendation
May 12, 2026Ziwei Liu, Yejing Wang, Shengyu Zhou +2Semantic ID Recommendation Representation Learning
City University of Hong Kong Hong Kong, China · Independent Researcher Beijing, China · Tsinghua University Beijing, China
Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors
May 12, 2026Wen-Ting Wang, Wei-Ying Wu, Hao-Yun Huang +1Representation Learning Residual Learning
Institute of Statistics, National Chung Hsing University · Department of Applied Mathematics, National Dong Hwa University · Department of Applied Mathematics National Dong Hwa University
TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning
May 12, 2026Fan Ma, Qier An, Peng Chen +6Representation Learning Self-Supervised Pre-Training
Department of Biomedical Informatics and Data Science, Yale University
Don't Look at the Numbers: Visual Anchoring Bias and Layer-wise Representation in VLMs
May 11, 2026M. ShalankinVLM Evaluation Vision-Language Models
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
May 11, 2026Alan Arazi, Eilam Shapira, Shoham Grunblat +8Tabular Foundation Models Representation Learning
Technion – Israel Institute of Technology · Prior Labs · NVIDIA +4
Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs
May 11, 2026Xuxiang Zhao, Angelica I. Aviles-RiveroFourier Neural Operator Representation Learning
Qiuzhen College, Tsinghua University · YMSC, Tsinghua University
Joint sparse coding and temporal dynamics support context reconfiguration
May 11, 2026Qianqian Shi, Yue Che, Faqiang Liu +6Representation Learning Continual Learning
Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China · Guangdong Institute of Intelligence Science and Technology, Hengqin, China · Optical Memory National Engineering Research Center, Tsinghua University, Beijing, China +3
Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning
May 11, 2026Yongmin Yoo, Qiongkai Xu, Zhangkai Wu +1Representation Learning Legal IR
Frontier AI Research Centre, Macquarie University
Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout
May 11, 2026Tianyang Chen, Mandar M. Sohoni, Saeed A. Khan +5Representation Learning
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA · School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA · USRA Research Institute for Advanced Computer Science, Mountain View, CA 94035, USA +1
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
May 10, 2026Siyu Wu, Cong Xu, Wei ZhangRepresentation Learning Interpretable ML
1Shanghai Institute of AI Education, East China Normal University, Shanghai, 200241, China · 2Shool of Computer Sicence and Technology, East China Normal University, Shanghai, 200241, China
Neural Information Causality
May 10, 2026Jeongho Bang, Marcin PawłowskiRepresentation Learning Actual Causality
Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul 03722, Republic of Korea · Department of Quantum Information, Yonsei University, Incheon 21983, Republic of Korea · International Centre for Theory of Quantum Technologies, University of Gda´nsk, 80-308 Gda´nsk, Poland +1
Sub-JEPA: Subspace Gaussian Regularization for Stable End-to-End World Models
May 10, 2026Kai Zhao, Dongliang Nie, Yuchen Lin +4Representation Learning Representation Collapse
1Shanghai University · 2The University of Manchester · 3Nankai University
Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning
May 9, 2026Arghamitra Talukder, Philippe Chlenski, Itsik Pe'erRepresentation Learning Representation Identifiability
Computer Science, Columbia University
SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
May 9, 2026Yan Sun, Guoxia Wang, Jinle Zeng +6Language Model Pretraining Contrastive Learning
Baidu Inc. · Nanyang Technological University
Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
May 9, 2026Nikhil J. Dhinagar, Vidhi Chhatbar, Chirag Jagad +6Representation Learning Spectral Methods
Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA · Neuroscience Graduate Program, Mark & Mary Stevens Neuroimaging & Informatics Institute, Chan Division of Occupational Science & Occupational Therapy, Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning
May 9, 2026James Hazelden, Laura Driscoll, Eli Shlizerman +1Representation Learning Neural Tangent Kernel
Applied Mathematics, University of Washington · Allen Institute · Neurobiology & Biophysics, University of Washington +1
Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
May 8, 2026Ali Syed, Aditya Nambiar, Jonathan W. SiegelRepresentation Learning Deep Sets
Department of Mathematics Texas A&M University College Station, TX 77843
It Just Takes Two: Scaling Amortized Inference to Large Sets
May 8, 2026Antoine Wehenkel, Michael Kagan, Lukas Heinrich +1Neural Posterior Estimation Representation Learning
Apple · SLAC National Accelerator Laboratory · TU München +1
Prototype Guided Post-pretraining for Single-Cell Representation Learning
May 8, 2026Sachini Weerasekara, Natasha Darras, Sagar Kamarthi +2Representation Learning Continued Pretraining
Sachini Weerasekara∗ · Natasha Darras · Sagar Kamarthi +2
Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer
May 8, 2026Gajendra Katuwal, Advait Koparkar, Salar Abbaspourazad +2Cross-Modal Learning Representation Learning
Apple
PersonaGest: Personalized Co-Speech Gesture Generation with Semantic-Guided Hierarchical Motion Representation
May 8, 2026Junchuan Zhao, Qifan Liang, Ye WangRepresentation Learning Human Motion Generation
School of Computing, National University of Singapore
Features have life history. And we should care
May 7, 2026Philipp Stecher, Sandro Radovanović, Vlasta Sikimić +1Representation Learning LLM Interpretability
University of Tübingen · University of Belgrade · Eindhoven University of Technology
Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning
May 7, 2026Yaozong Zheng, Qihua Liang, Bineng Zhong +4Representation Learning Self-Supervised Visual Representation Learning
Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education Guangxi Normal University, Guilin 541004, China · University Engineering Research Center of Educational Intelligent Technology Guangxi Normal University, Guilin 541004, China · University of Southampton, Southampton, SO17 1BJ, United Kingdom +1
CRISP: Compositional Relations as Invariant Structural Priors for Domain Generalization
May 7, 2026Dat Nguyen, Duc-Duy NguyenRepresentation Learning Visual Representation Learning
Harvard University Basis Research Institute · Hanoi University of Science and Technology
Wisteria: A Unified Multi-Scale Feature Learning Framework for DNA Language Model
May 7, 2026Weihua Wang, Haoji Li, Feilong Bao +2Representation Learning Language Modeling
College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia, 010021, China · National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, Hohhot, Inner Mongolia, 010021, China · Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology, Hohhot, Inner Mongolia, 010021, China +1
Jointly Learning Structured Representations and Stabilized Affinity for Human Motion Segmentation
May 7, 2026Xianghan Meng, Zhiyuan Huang, Zhengyu Tong +1Representation Learning Clustering
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China

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Cross-Modal-Domain Generalization Through Semantically Aligned Discrete Representations

Souptik Sen, Raneen Younis, Zahra Ahmadi

Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School, Germany · Lower Saxony Center for AI and Causal Methods in Medicine (CAIMed), Hannover, Germany

Multimodal Learning Representation Learning Cross-Modal Representation Learning Multimodal Domain Generalization Cross-Modal Alignment Multimodal SSL+3

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Multimodal learning seeks to integrate information across diverse sensory sources, yet current approaches struggle to balance cross-modal generalizability with modality-specific structure. Continuous (implicit) methods preserve fine-grained priors but render generalization challenging, while discrete (explicit) approaches enforce shared prototypes at the expense of modality specificity. We introduce CoDAAR (Cross-modal Discrete Alignment And Reconstruction), a novel framework that resolves this long-standing trade-off by establishing semantic consensus across modality-specific codebooks through index-level alignment. This design uniquely allows CoDAAR to preserve modality-unique structures while achieving generalizable cross-modal representations within a unified discrete space. CoDAAR combines two complementary mechanisms: Discrete Temporal Alignment (DTA), which enables fine-grained temporal quantization, and Cascading Semantic Alignment (CSA), which promotes progressive cross-modal semantic agreement. Together, they establish a competition-free unified representation space. Trained with self-supervised reconstruction objectives on paired multimodal sequences, CoDAAR demonstrates robust cross-modal and cross-domain generalization. Across Cross-Modal Generalization benchmarks, including event classification, localization, video segmentation, and cross-dataset transfer, CoDAAR achieves state-of-the-art performance, establishing a new paradigm for discrete and generalizable multimodal representation learning.