1.1k citations · 4.4k across the 113 of their papers we have counts for
14 papers · 1 filter
Augmenting Convolutional networks with attention-based aggregation
Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby +4
We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning. We replace the final average pooling by an attention-based aggre…
Pseudo-Labeling for Massively Multilingual Speech Recognition
Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve +1
Semi-supervised learning through pseudo-labeling has become a staple of state-of-the-art monolingual speech recognition systems. In this work, we extend pseudo-labeling to massivel…
Hierarchical Skills for Efficient Exploration
Jonas Gehring, Gabriel Synnaeve, Andreas Krause +1
In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike…
Word Order Does Not Matter For Speech Recognition
Vineel Pratap, Qiantong Xu, Tatiana Likhomanenko +2
In this paper, we study training of automatic speech recognition system in a weakly supervised setting where the order of words in transcript labels of the audio training data is n…
ASR4REAL: An extended benchmark for speech models
Morgane Riviere, Jade Copet, Gabriel Synnaeve
Popular ASR benchmarks such as Librispeech and Switchboard are limited in the diversity of settings and speakers they represent. We introduce a set of benchmarks matching real-life…
Leveraging Automated Unit Tests for Unsupervised Code Translation
Baptiste Roziere, Jie M. Zhang, Francois Charton +3
With little to no parallel data available for programming languages, unsupervised methods are well-suited to source code translation. However, the majority of unsupervised machine…
XCiT: Cross-Covariance Image Transformers
Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron +8
Following their success in natural language processing, transformers have recently shown much promise for computer vision. The self-attention operation underlying transformers yiel…
ResMLP: Feedforward networks for image classification with data-efficient training
Hugo Touvron, Piotr Bojanowski, Mathilde Caron +8
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which…
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings
Tatiana Likhomanenko, Qiantong Xu, Gabriel Synnaeve +2
Without positional information, attention-based Transformer neural networks are permutation-invariant. Absolute or relative positional embeddings are the most popular ways to feed…
Going deeper with Image Transformers
Hugo Touvron, Matthieu Cord, Alexandre Sablayrolles +2
Transformers have been recently adapted for large scale image classification, achieving high scores shaking up the long supremacy of convolutional neural networks. However the opti…
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun +3
Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, tr…
Differentiable Model Compression via Pseudo Quantization Noise
Alexandre Défossez, Yossi Adi, Gabriel Synnaeve
We propose DiffQ a differentiable method for model compression for quantizing model parameters without gradient approximations (e.g., Straight Through Estimator). We suggest adding…
Gradient Matching for Domain Generalization
Yuge Shi, Jeffrey Seely, Philip H. S. Torr +4
Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their…
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski +8
Self-supervised learning of speech representations has been a very active research area but most work is focused on a single domain such as read audio books for which there exist l…