Leon Bottou

is this you? claim profile

∙ 0 followers
Facebook

Diplôme d'Ingénieur from the École Polytechnique (X84) in 1987, the Master of Mathematics, Applied Mathematics and Computer Science from Ecole Normale Supérieure in 1988, and a PhD in computer science from University of Paris-Sud in 1991 I went to AT & T Bell Laboratories, AT & T Labs, NEC Labs America, and Microsoft Research. I joined the Facebook AI Research in March 2015.

Featured Co-authors

Bernhard Schölkopf
189 publications
Francis Bach
132 publications
Yann LeCun
86 publications
Alekh Agarwal
54 publications
Jonas Peters
37 publications
David Grangier
36 publications
Nicolas Usunier
31 publications
Rachel Ward
29 publications
David Lopez-Paz
28 publications
Yann Ollivier
28 publications
Aaron Defazio
22 publications

An Attract-Repel Decomposition of Undirected Networks

Dot product latent space embedding is a common form of representation le...
06/17/2021 ∙ by Alexander Peysakhovich, et al. ∙ 0 ∙ share

read it
On the Convergence of Adam and Adagrad

We provide a simple proof of the convergence of the optimization algorit...
03/05/2020 ∙ by Alexandre Défossez, et al. ∙ 19 ∙ share

read it
Music Source Separation in the Waveform Domain

Source separation for music is the task of isolating contributions, or s...
11/27/2019 ∙ by Alexandre Défossez, et al. ∙ 0 ∙ share

read it
Symplectic Recurrent Neural Networks

We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algo...
09/29/2019 ∙ by Zhengdao Chen, et al. ∙ 0 ∙ share

read it
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed

We study the problem of source separation for music using deep learning ...
09/03/2019 ∙ by Alexandre Défossez, et al. ∙ 5 ∙ share

read it
Invariant Risk Minimization

We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
07/05/2019 ∙ by Martin Arjovsky, et al. ∙ 1 ∙ share

read it
Scaling Laws for the Principled Design, Initialization and Preconditioning of ReLU Networks

In this work, we describe a set of rules for the design and initializati...
06/10/2019 ∙ by Aaron Defazio, et al. ∙ 1 ∙ share

read it
Cold Case: The Lost MNIST Digits

Although the popular MNIST dataset [LeCun et al., 1994] is derived from ...
05/25/2019 ∙ by Chhavi Yadav, et al. ∙ 0 ∙ share

read it
Controlling Covariate Shift using Equilibrium Normalization of Weights

We introduce a new normalization technique that exhibits the fast conver...
12/11/2018 ∙ by Aaron Defazio, et al. ∙ 1 ∙ share

read it
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning

The application of stochastic variance reduction to optimization has sho...
12/11/2018 ∙ by Aaron Defazio, et al. ∙ 1 ∙ share

read it
SING: Symbol-to-Instrument Neural Generator

Recent progress in deep learning for audio synthesis opens the way to mo...
10/23/2018 ∙ by Alexandre Défossez, et al. ∙ 0 ∙ share

read it
AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization

Adaptive gradient methods such as AdaGrad and its variants update the st...
06/05/2018 ∙ by Rachel Ward, et al. ∙ 0 ∙ share

read it
WNGrad: Learn the Learning Rate in Gradient Descent

Adjusting the learning rate schedule in stochastic gradient methods is a...
03/07/2018 ∙ by Xiaoxia Wu, et al. ∙ 0 ∙ share

read it
Adversarial Vulnerability of Neural Networks Increases With Input Dimension

Over the past four years, neural networks have proven vulnerable to adve...
02/05/2018 ∙ by Carl-Johann Simon-Gabriel, et al. ∙ 0 ∙ share

read it
Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variet...
12/21/2017 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

read it
Diagonal Rescaling For Neural Networks

We define a second-order neural network stochastic gradient training alg...
05/25/2017 ∙ by Jean Lafond, et al. ∙ 0 ∙ share

read it
Wasserstein GAN

We introduce a new algorithm named WGAN, an alternative to traditional G...
01/26/2017 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ share

read it
Towards Principled Methods for Training Generative Adversarial Networks

The goal of this paper is not to introduce a single algorithm or method,...
01/17/2017 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ share

read it
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

We look at the eigenvalues of the Hessian of a loss function before and ...
11/22/2016 ∙ by Levent Sagun, et al. ∙ 0 ∙ share

read it
Optimization Methods for Large-Scale Machine Learning

This paper provides a review and commentary on the past, present, and fu...
06/15/2016 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

read it
Discovering Causal Signals in Images

This paper establishes the existence of observable footprints that revea...
05/26/2016 ∙ by David Lopez-Paz, et al. ∙ 0 ∙ share

read it
Unifying distillation and privileged information

Distillation (Hinton et al., 2015) and privileged information (Vapnik & ...
11/11/2015 ∙ by David Lopez-Paz, et al. ∙ 0 ∙ share

read it
No Regret Bound for Extreme Bandits

Algorithms for hyperparameter optimization abound, all of which work wel...
08/12/2015 ∙ by Robert Nishihara, et al. ∙ 0 ∙ share

read it
A Lower Bound for the Optimization of Finite Sums

This paper presents a lower bound for optimizing a finite sum of n funct...
10/02/2014 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

read it
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems

Quick interaction between a human teacher and a learning machine present...
09/16/2014 ∙ by Patrice Simard, et al. ∙ 0 ∙ share

read it
Counterfactual Reasoning and Learning Systems

This work shows how to leverage causal inference to understand the behav...
09/11/2012 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

read it
From Machine Learning to Machine Reasoning

A plausible definition of "reasoning" could be "algebraically manipulati...
02/09/2011 ∙ by Leon Bottou, et al. ∙ 0 ∙ share

read it