Leon Bottou

Léon received the Diplôme d’Ingénieur de l’École Polytechnique (X84), the Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Supérieure, and a Ph.D. in Computer Science from Université de Paris-Sud. His research career has taken him to AT&T Bell Laboratories, AT&T Labs Research, NEC Labs America, and Microsoft. Léon then joined Facebook AI Research. The long-term goal of Léon's research is to understand and replicate human-level intelligence. Because this goal requires conceptual advances that cannot be anticipated, Léon’s research has followed many practical and theoretical turns including neural networks applications, stochastic gradient learning algorithms, statistical properties of learning systems, computer vision applications with structured outputs, and theory of large-scale learning. Léon's research aims to clarify the relation between learning and reasoning, with focus on the many aspects of causation (inference, invariance, reasoning, affordance, and intuition).

Leon's Publications

April 07, 2020

RESEARCH

THEORY

First-order Adversarial Vulnerability of Neural Networks and Input Dimension

Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients…

Carl-Johann Simon-Gabriel, Yann Ollivier, Bernhard Scholkopf, Leon Bottou, David Lopez-Paz,

April 07, 2020

April 07, 2020

RESEARCH

SPEECH & AUDIO

SING: Symbol-to-Instrument Neural Generator

Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite…

Alexandre Défossez, Neil Zeghidour, Nicolas Usunier, Leon Bottou, Francis Bach,

April 07, 2020

April 07, 2020

RESEARCH

COMPUTER VISION

Optimization Methods for Large-Scale Machine Learning

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural…

Leon Bottou, Frank E. Curtis, Jorge Nocedal,

April 07, 2020