Fetching the paper…
Reading the bibliography…
We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs).
The nature of explanation , volume 445
Kenneth James Williams Craik · 1967
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities
J J Hopfield · 1982
Earlier work this paper cites.
Making the world differentiable: on using self supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments
Jurgen Schmidhuber · 1990
Earlier work this paper cites.
Dyna, an integrated architecture for learning, planning, and reacting
Richard S. Sutton · 1991
Earlier work this paper cites.
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
R. P. Rao and D. H. Ballard · 1999
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Geoffrey E. Hinton · 2002
Earlier work this paper cites.
Tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the echo state network approach
Herbert Jaeger · 2002
Earlier work this paper cites.
A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
Earlier work this paper cites.
Juergen Schmidhuber · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Model predictive path integral control using covariance variable importance sampling, 2015
Grady Williams, Andrew Aldrich, and Evangelos Theodorou · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
Earlier work this paper cites.
IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, and Koray Kavukcuoglu · 2018
Earlier work this paper cites.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, and Sergey Levine · 2018
Earlier work this paper cites.
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
Earlier work this paper cites.
Hierarchical reinforcement learning with hindsight
Andrew Levy, Robert Platt, and Kate Saenko · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
Cited alongside, same era.
Videomae v2: Scaling video masked autoencoders with dual masking
Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, and Yu Qiao · 2023
Later among the works it cites.
How learning by reconstruction produces uninformative features for perception
Randall Balestriero and Yann Lecun · 2024
Later among the works it cites.
Revisiting feature prediction for learning visual representations from video
Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann LeCun, Mido Assran, and Nicolas Ballas · 2024
Later among the works it cites.
Video generation models as world simulators, 2024
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, et al · 2024
Later among the works it cites.
Genie: Generative interactive environments
Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al · 2024
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
Cited alongside, same era.
Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods
Randall Balestriero and Yann LeCun · 2022
Cited alongside, same era.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2022
Cited alongside, same era.
Deep hierarchical planning from pixels
Danijar Hafner, Kuang-Huei Lee, Ian Fischer, and Pieter Abbeel · 2022
Cited alongside, same era.
Temporal difference learning for model predictive control
Nicklas A Hansen, Hao Su, and Xiaolong Wang · 2022
Cited alongside, same era.
Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine · 2022
Cited alongside, same era.
A path towards autonomous machine intelligence
Yann LeCun · 2022
Cited alongside, same era.
Learning and leveraging world models in visual representation learning, 2024
Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, and Yann LeCun · 2024
Later among the works it cites.
Mastering diverse domains through world models, 2024
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2024
Later among the works it cites.
Td-mpc2: Scalable, robust world models for continuous control
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
Later among the works it cites.
DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2024
Later among the works it cites.
Genie 2: A large-scale foundation world model
Jack Parker-Holder, Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung, Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan, Maxime Gazeau, Bonnie Li, Fabio Pardo, Luyu Wang, Lei Zhang, Frederic Besse, Tim Harley, Anna Mitenkova, Jane Wang, Jeff Clune, Demis Hassabis, Raia Hadsell, Adrian Bolton, Satinder Singh, and Tim Rocktäschel · 2024
Later among the works it cites.
V-jepa 2: Self-supervised video models enable understanding, prediction and planning, 2025
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba, Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, and Nicolas Ballas · 2025
Later among the works it cites.
Lejepa: Provable and scalable self-supervised learning without the heuristics, 2025
Randall Balestriero and Yann LeCun · 2025
Later among the works it cites.
stable-pretraining-v1: Foundation model research made simple, 2025
Randall Balestriero, Hugues Van Assel, Sami BuGhanem, and Lucas Maes · 2025
Later among the works it cites.
Navigation world models
Amir Bar, Gaoyue Zhou, Danny Tran, Trevor Darrell, and Yann LeCun · 2025
Later among the works it cites.
Intuitive physics understanding emerges from self-supervised pretraining on natural videos, 2025
Quentin Garrido, Nicolas Ballas, Mahmoud Assran, Adrien Bardes, Laurent Najman, Michael Rabbat, Emmanuel Dupoux, and Yann LeCun · 2025
Later among the works it cites.
Learning from reward-free offline data: A case for planning with latent dynamics models, 02 2025
Vlad Sobal, Wancong Zhang, Kyunghyun Cho, Randall Balestriero, Tim Rudner, and Yann LeCun · 2025
Later among the works it cites.
What drives success in physical planning with joint-embedding predictive world models?, 2026
Basile Terver, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, and Yann LeCun · 2026
Closest in time.