1.3k citations · 2.2k across the 28 of their papers we have counts for
5 papers · 1 filter
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea +12
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly u…
Perceiver IO: A General Architecture for Structured Inputs & Outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac +12
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied…
Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang +3
Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets…
SEARNN: Training RNNs with Global-Local Losses
Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin +1
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction. RNNs have been wide…
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz +2
In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured suppo…