3.4k citations · 4.7k across the 28 of their papers we have counts for
6 papers · 1 filter
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…
Supervised Seeded Iterated Learning for Interactive Language Learning
Yuchen Lu, Soumye Singhal, Florian Strub +2
Language drift has been one of the major obstacles to train language models through interaction. When word-based conversational agents are trained towards completing a task, they t…
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning
Mathieu Seurin, Florian Strub, Philippe Preux +1
Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In th…
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…
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…
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub +2
Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. Nevertheless, as soon as the agents are f…