2.6k citations · 5k across the 50 of their papers we have counts for
7 papers · 1 filter
Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
Aaqib Saeed, David Grangier, Olivier Pietquin +1
We propose CHARM, a method for training a single neural network across inconsistent input channels. Our work is motivated by Electroencephalography (EEG), where data collection pro…
Contrastive Learning of General-Purpose Audio Representations
Aaqib Saeed, David Grangier, Neil Zeghidour
We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a repres…
Human-Paraphrased References Improve Neural Machine Translation
Markus Freitag, George Foster, David Grangier +1
Automatic evaluation comparing candidate translations to human-generated paraphrases of reference translations has recently been proposed by Freitag et al. When used in place of or…
Toward Better Storylines with Sentence-Level Language Models
Daphne Ippolito, David Grangier, Douglas Eck +1
We propose a sentence-level language model which selects the next sentence in a story from a finite set of fluent alternatives. Since it does not need to model fluency, the sentenc…
BLEU might be Guilty but References are not Innocent
Markus Freitag, David Grangier, Isaac Caswell
The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice…
Efficient Content-Based Sparse Attention with Routing Transformers
Aurko Roy, Mohammad Saffar, Ashish Vaswani +1
Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic compute and memory require…