
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 ∙ shareread 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 ∙ shareread it

Diagonal Rescaling For Neural Networks
We define a secondorder neural network stochastic gradient training alg...
05/25/2017 ∙ by Jean Lafond, et al. ∙ 0 ∙ shareread it

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

Optimization Methods for LargeScale Machine Learning
This paper provides a review and commentary on the past, present, and fu...
06/15/2016 ∙ by Leon Bottou, et al. ∙ 0 ∙ shareread it

ICE: Enabling NonExperts to Build Models Interactively for LargeScale Lopsided Problems
Quick interaction between a human teacher and a learning machine present...
09/16/2014 ∙ by Patrice Simard, et al. ∙ 0 ∙ shareread it

Unifying distillation and privileged information
Distillation (Hinton et al., 2015) and privileged information (Vapnik & ...
11/11/2015 ∙ by David LopezPaz, et al. ∙ 0 ∙ shareread 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 ∙ shareread it

Discovering Causal Signals in Images
This paper establishes the existence of observable footprints that revea...
05/26/2016 ∙ by David LopezPaz, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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 CarlJohann SimonGabriel, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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 ∙ shareread 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. ∙ 0 ∙ shareread 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. ∙ 0 ∙ shareread it

SING: SymboltoInstrument Neural Generator
Recent progress in deep learning for audio synthesis opens the way to mo...
10/23/2018 ∙ by Alexandre Défossez, et al. ∙ 0 ∙ shareread 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 ∙ shareread 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. ∙ 0 ∙ shareread it

Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
07/05/2019 ∙ by Martin Arjovsky, et al. ∙ 0 ∙ shareread it

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