works on

From the 1 of 140 linked papers with an AI index.

activity
20102026
most citedGeometric deep learning: going beyond Euclidean data

3.7k citations · 25.2k across the 137 of their papers we have counts for

collaborators

140 papers

cs.LG2026

Advancing Open and Reproducible Relational Learning: RelArena-, TabPFN-Rel and RPI

Adrian Hayler, Klemens Flöge, Alan Arazi +44

This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate researc…

cs.CV2024★ 7 cited

Learning and Leveraging World Models in Visual Representation Learning

Quentin Garrido, Mahmoud Assran, Nicolas Ballas +3

Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns by leveraging a world model. While previously limited to predicting m…

cs.LG2024

Fast and Exact Enumeration of Deep Networks Partitions Regions

Randall Balestriero, Yann LeCun

One fruitful formulation of Deep Networks (DNs) enabling their theoretical study and providing practical guidelines to practitioners relies on Piecewise Affine Splines. In that rea…

cs.CV2023

URLOST: Unsupervised Representation Learning without Stationarity or Topology

Zeyu Yun, Juexiao Zhang, Yann LeCun +1

Unsupervised representation learning has seen tremendous progress. However, it is constrained by its reliance on domain specific stationarity and topology, a limitation not found i…

cs.CV2023

Stochastic positional embeddings improve masked image modeling

Amir Bar, Florian Bordes, Assaf Shocher +6

Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent success, learning good representations…

cs.CV2023★ 5 cited

MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features

Adrien Bardes, Jean Ponce, Yann LeCun

Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and diff…