David Lopez-Paz

David is a research scientist at Facebook AI Research (FAIR), where he develops theory and algorithms to discover causation from data, in order to create robust learning machines.

David's Publications

April 07, 2020

RESEARCH

COMPUTER VISION

Manifold Mixup: Better Representations by Interpolating Hidden States

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To…

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio,

April 07, 2020

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

COMPUTER VISION

mixup: Beyond Empirical Risk Minimization

Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup…

Hongyi Zhang, Moustapha Cisse, Yann Dauphin, David Lopez-Paz,

April 07, 2020