Predictive Representation Learning

Follow this topic Ask about this topic

Momentum

23 papers in the last four weeks, up 156% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 116

All topics

CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision
May 13, 2026Xuanhao Luo, Zhizhen Li, Yuchen LiuSelf-Supervised Pre-Training Self-Supervised Time Series Representation Learning
North Carolina State University, USA
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
Forecast-aware Gaussian Splatting for Predictive 3D Representation in Language-Guided Pick-and-Place Manipulation
May 11, 2026Kaixin Jia, Jiacheng Xu3D Gaussian Splatting Language-Conditioned Robot Manipulation
KTH Royal Institute of Technology, Stockholm, Sweden.
Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories
May 11, 2026Yixuan Yang, Mehak Arora, Ryan Zhang +10Self-Supervised Pre-Training Healthcare
Duke University, Durham, NC, USA
MSPR: Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
May 10, 2026Valliappan Chidambaram Adaikkappan, Sai Rajeswar, Pietro Mazzaglia +1Offline RL Predictive Representation Learning
Mila, McGill University · Qualcomm Research
Towards Effective Theory of LLMs: A Representation Learning Approach
May 10, 2026Muhammed Ustaomeroglu, Guannan QuLLM Interpretability Mechanistic Interpretability
Carnegie Mellon University Pittsburgh, PA 15213
3D MRI Image Pretraining via Controllable 2D Slice Navigation Task
May 7, 2026Yu Wang, Qingchao ChenVisual Representation Learning Self-Supervised Pre-Training
Beijing University of Posts and Telecommunications · Peking University
AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
May 7, 2026Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo +8Joint-Embedding Predictive Architecture PDE Surrogate Modeling
Universidad Politécnica de Madrid · University of Michigan · Universitat Politècnica de València +3
The Predictive-Causal Gap: An Impossibility Theorem and Large-Scale Neural Evidence
May 6, 2026Kejun LiuCausal Representation Learning Predictive Representation Learning
State Key Laboratory of Bioinspired Interface Material Science, Institute of Nano & Functional Materials, Soochow University, Suzhou 215123, China
Understanding Self-Supervised Learning via Latent Distribution Matching
May 5, 2026Fabian A Mikulasch, Friedemann ZenkeRepresentation Learning Self-Supervised Learning
Friedrich Miescher Institute for Biomedical Research, 4056 Basel, Switzerland · Faculty of Science, University of Basel, 4033 Basel, Switzerland
Text-Conditional JEPA for Learning Semantically Rich Visual Representations
May 5, 2026Chen Huang, Xianhang Li, Vimal Thilak +2Joint-Embedding Predictive Architecture Predictive Representation Learning
Apple, Cupertino, United States
Latent State Design for World Models under Sufficiency Constraints
May 3, 2026Keon Woo KimRepresentation Learning Predictive Representation Learning
Om Labs
CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
May 1, 2026Hada Melino Muhammad, Zechen Li, Flora Salim +1Cross-Modal Representation Learning Self-Supervised Time Series Representation Learning
University of New South Wales · Google Research
Why Self-Supervised Encoders Want to Be Normal
Apr 30, 2026Yuval DombRepresentation Collapse Self-Supervised Learning
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
Apr 27, 2026K. Michael Martini, Eslam Abdelaleem, Paarth Gulati +1Dynamical Systems Information Bottleneck
Department of Physics, Emory University, Atlanta, GA, USA · Schools of Physics and Psychology, Georgia Institute of Technology, Atlanta, GA, USA · Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA, USA +1
A General Representation-Based Approach to Multi-Source Domain Adaptation
Apr 26, 2026Ignavier Ng, Yan Li, Zijian Li +3Multi-Source Domain Adaptation Unsupervised Domain Adaptation
Carnegie Mellon University · Mohamed bin Zayed University of Artificial Intelligence
Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
Apr 24, 2026Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner +1Latent Dynamics Modeling Healthcare
Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte, Brazil
Self-Predictive Representation for Autonomous UAV Object-Goal Navigation
Apr 22, 2026Angel Ayala, Donling Sui, Francisco Cruz +3UAV Navigation Predictive Representation Learning
Escola Polit´ecnica de Pernambuco, Universidade de Pernambuco, Recife, Brasil · School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia · Escuela de Ingenier´ıa, Universidad Central de Chile, Santiago, Chile
Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
Apr 20, 2026Blaise Delaney, Salil Patel, Yuji Xing +3Medical Diagnosis Predictive Representation Learning
1TimeTrace Labs · 2NVIDIA · University of Oxford
Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning
Apr 16, 2026Aimen BoukhariRepresentation Geometry Masked Language Modeling
´Ecole Nationale Sup´erieure d’Informatique (ESI), Algiers , Algeria
Cross-Sectional Asset Retrieval via Future-Aligned Soft Contrastive Learning
Feb 11, 2026Hyeongmin Lee, Chanyeol Choi, Jihoon Kwon +8Contrastive Learning Information Retrieval
Seoul National University of Science and Technology Seoul, Republic of Korea · LinqAlpha Cambridge, MA, United States · Massachusetts Institute of Technology Cambridge, MA, United States +7
Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex
Nov 16, 2025Sushrut Thorat, Adrien Doerig, Alexander Kroner +2Predictive Coding Visual Neuroscience
Institute of Cognitive Science, Osnabrück University, Osnabrück 49069, Germany. · Lossfunk, Bengaluru 560038, India. · Department of Education and Psychology, Freie Universität, Berlin 14195, Germany. +1
Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization
Apr 8, 2025Enze Shi, Yiqun Xiao, Linglong Kong +1Representation Learning Algorithmic Fairness
Department of Mathematical and Statistical Science, University of Alberta.
Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning
Mar 22, 2025Alexander Ororbia, Karl Friston, Rajesh P. N. RaoSelf-Supervised Learning Predictive Coding
Rochester Institute of Technology · Rochester, New York, USA · VERSES AI Research Lab +3
Stochastic Optimal Control for Continuous-Time fMRI Representation Learning
Feb 7, 2025Joonhyeong Park, Byoungwoo Park, Chang-Bae Bang +4Latent Dynamics Modeling Self-Supervised Time Series Representation Learning
KAIST · Yonsei University · EverEx +1
Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization
Date pendingInhyeok Lee, Luke Solo, Michael C. Burkhart +5Healthcare Predictive Representation Learning
University of Chicago

May 13, 2026, cs.LGJ/K move · Enter open · S save

CSI-JEPA: Towards Foundation Representations for Ubiquitous Sensing with Minimal Supervision

Xuanhao Luo, Zhizhen Li, Yuchen Liu

North Carolina State University, USA

Self-Supervised Pre-Training Self-Supervised Time Series Representation Learning Multi-Task Learning Predictive Representation Learning Wi-Fi Sensing

Open paper AskPDF arXiv

Channel state information (CSI) provides a widely available sensing modality for human and environment perception, but existing CSI sensing models usually rely on task-specific supervised training and require substantial labeled data for each task, device, user, or environment. This limits their scalability in practical deployments where unlabeled CSI is abundant but labeled data is costly to collect. In this paper, we present CSI-JEPA, a self-supervised predictive representation learning framework for label-efficient, multi-task Wi-Fi sensing. CSI-JEPA learns reusable temporal-spectral representations from unlabeled CSI samples by predicting latent features of masked channel regions from visible context. To better match the physical structure of CSI, CSI-JEPA tokenizes channel-response amplitude windows along the time and subcarrier dimensions. It then introduces a channel variation-aware masking strategy that samples predictive targets from regions with stronger local temporal and subcarrier-domain variations. After pretraining, the encoder is frozen and used as a backbone, with lightweight task-specific adapters added for downstream sensing tasks. We evaluate CSI-JEPA on seven real-world Wi-Fi sensing tasks spanning diverse objectives and deployment settings. The results show that CSI-JEPA improves downstream sensing performance over competitive baselines, achieving up to 10.64 percentage points mean accuracy gain over state-of-the-art supervised Transformer and matched-budget label savings of up to 98.0%.

Momentum

Related topics

Latest papers 116