no code implementations • 1 Mar 2024 • Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, Yann Lecun
Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model.
1 code implementation • arXiv preprint 2024 • Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann Lecun, Mahmoud Assran, Nicolas Ballas
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision.
1 code implementation • NeurIPS 2023 • Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann Lecun, Bobak T. Kiani
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering.
no code implementations • 24 Apr 2023 • Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann Lecun, Micah Goldblum
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.
1 code implementation • 14 Feb 2023 • Quentin Garrido, Laurent Najman, Yann Lecun
We hope that both our introduced dataset and approach will enable learning richer representations without supervision in more complex scenarios.
no code implementations • 24 Oct 2022 • Mark Ibrahim, Quentin Garrido, Ari Morcos, Diane Bouchacourt
We study not only how robust recent state-of-the-art models are, but also the extent to which models can generalize variation in factors when they're present during training.
no code implementations • 5 Oct 2022 • Quentin Garrido, Randall Balestriero, Laurent Najman, Yann Lecun
Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid development, with the emergence of many method variations but only few principled guidelines that would help practitioners to successfully deploy them.
no code implementations • 27 Jun 2022 • Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent
This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream.
no code implementations • 3 Jun 2022 • Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, Yann Lecun
Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches.
1 code implementation • 11 Feb 2021 • Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred Hamprecht
Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution.