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20162023
most citedBootstrap your own latent: A new approach to self-supervised Learning

3.4k citations · 4.7k across the 28 of their papers we have counts for

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6 papers · 1 filter

cs.LG2023★ 1 cited

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Pierre H. Richemond, Allison Tam, Yunhao Tang +3

Self-predictive unsupervised learning methods such as BYOL or SimSiam have shown impressive results, and counter-intuitively, do not collapse to trivial representations. In this wo…

cs.LG2021★ 1 cited

Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness

Mathieu Seurin, Florian Strub, Philippe Preux +1

Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alle…

cs.LG2020★ 3 cited

The Monte Carlo Transformer: a stochastic self-attention model for sequence prediction

Alice Martin, Charles Ollion, Florian Strub +2

This paper introduces the Sequential Monte Carlo Transformer, an original approach that naturally captures the observations distribution in a transformer architecture. The keys, qu…

cs.LG2020★ 3.4k cited

Bootstrap your own latent: A new approach to self-supervised Learning

Jean-Bastien Grill, Florian Strub, Florent Altché +11

We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target…

cs.LG2019★ 15 cited

HIGhER : Improving instruction following with Hindsight Generation for Experience Replay

Geoffrey Cideron, Mathieu Seurin, Florian Strub +1

Language creates a compact representation of the world and allows the description of unlimited situations and objectives through compositionality. While these characterizations may…

cs.LG2016★ 168 cited

Hybrid Recommender System based on Autoencoders

Florian Strub, Romaric Gaudel, Jérémie Mary

A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratin…