
An AttractRepel Decomposition of Undirected Networks
Dot product latent space embedding is a common form of representation le...
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On the Convergence of Adam and Adagrad
We provide a simple proof of the convergence of the optimization algorit...
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Music Source Separation in the Waveform Domain
Source separation for music is the task of isolating contributions, or s...
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Symplectic Recurrent Neural Networks
We propose Symplectic Recurrent Neural Networks (SRNNs) as learning algo...
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Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed
We study the problem of source separation for music using deep learning ...
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Invariant Risk Minimization
We introduce Invariant Risk Minimization (IRM), a learning paradigm to e...
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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...
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Cold Case: The Lost MNIST Digits
Although the popular MNIST dataset [LeCun et al., 1994] is derived from ...
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Controlling Covariate Shift using Equilibrium Normalization of Weights
We introduce a new normalization technique that exhibits the fast conver...
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On the Ineffectiveness of Variance Reduced Optimization for Deep Learning
The application of stochastic variance reduction to optimization has sho...
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SING: SymboltoInstrument Neural Generator
Recent progress in deep learning for audio synthesis opens the way to mo...
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AdaGrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization
Adaptive gradient methods such as AdaGrad and its variants update the st...
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WNGrad: Learn the Learning Rate in Gradient Descent
Adjusting the learning rate schedule in stochastic gradient methods is a...
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Adversarial Vulnerability of Neural Networks Increases With Input Dimension
Over the past four years, neural networks have proven vulnerable to adve...
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Geometrical Insights for Implicit Generative Modeling
Learning algorithms for implicit generative models can optimize a variet...
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Diagonal Rescaling For Neural Networks
We define a secondorder neural network stochastic gradient training alg...
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Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional G...
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Towards Principled Methods for Training Generative Adversarial Networks
The goal of this paper is not to introduce a single algorithm or method,...
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Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
We look at the eigenvalues of the Hessian of a loss function before and ...
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Optimization Methods for LargeScale Machine Learning
This paper provides a review and commentary on the past, present, and fu...
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Discovering Causal Signals in Images
This paper establishes the existence of observable footprints that revea...
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Unifying distillation and privileged information
Distillation (Hinton et al., 2015) and privileged information (Vapnik & ...
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No Regret Bound for Extreme Bandits
Algorithms for hyperparameter optimization abound, all of which work wel...
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A Lower Bound for the Optimization of Finite Sums
This paper presents a lower bound for optimizing a finite sum of n funct...
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ICE: Enabling NonExperts to Build Models Interactively for LargeScale Lopsided Problems
Quick interaction between a human teacher and a learning machine present...
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Counterfactual Reasoning and Learning Systems
This work shows how to leverage causal inference to understand the behav...
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From Machine Learning to Machine Reasoning
A plausible definition of "reasoning" could be "algebraically manipulati...
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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.