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Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbatio…
Qualitatively characterizing neural network optimization problems
Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe
Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and ot…
On distinguishability criteria for estimating generative models
Ian J. Goodfellow
Two recently introduced criteria for estimation of generative models are both based on a reduction to binary classification. Noise-contrastive estimation (NCE) is an estimation pro…
An empirical analysis of dropout in piecewise linear networks
David Warde-Farley, Ian J. Goodfellow, Aaron Courville +1
The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, a…