43 citations · 190 across the 11 of their papers we have counts for
12 papers
AudioLM: a Language Modeling Approach to Audio Generation
Zalán Borsos, Raphaël Marinier, Damien Vincent +8
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio genera…
Learning strides in convolutional neural networks
Rachid Riad, Olivier Teboul, David Grangier +1
Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermed…
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian +3
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms. The toolbox builds on various JAX features, suc…
DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding
Neil Zeghidour, Olivier Teboul, David Grangier
We introduce DIVE, an end-to-end speaker diarization algorithm. Our neural algorithm presents the diarization task as an iterative process: it repeatedly builds a representation fo…
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking
Andrew N Carr, Quentin Berthet, Mathieu Blondel +2
Self-supervised pre-training using so-called "pretext" tasks has recently shown impressive performance across a wide range of modalities. In this work, we advance self-supervised l…
LEAF: A Learnable Frontend for Audio Classification
Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry +1
Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeni…
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
Marco Cuturi, Olivier Teboul, Quentin Berthet +2
When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in…
Fast Differentiable Sorting and Ranking
Mathieu Blondel, Olivier Teboul, Quentin Berthet +1
The sorting operation is one of the most commonly used building blocks in computer programming. In machine learning, it is often used for robust statistics. However, seen as a func…
Learning with Differentiable Perturbed Optimizers
Quentin Berthet, Mathieu Blondel, Olivier Teboul +3
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete d…
Supervised Quantile Normalization for Low-rank Matrix Approximation
Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed +1
Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts. To be robust…
MULEX: Disentangling Exploitation from Exploration in Deep RL
Lucas Beyer, Damien Vincent, Olivier Teboul +3
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the…
Differentiable Ranks and Sorting using Optimal Transport
Marco Cuturi, Olivier Teboul, Jean-Philippe Vert
Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select…