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
June 09, 2019
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,
June 09, 2019
June 09, 2019
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,
June 09, 2019
April 30, 2018
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 30, 2018
