153 citations · 165 across the 4 of their papers we have counts for
3 papers · 1 filter
Adversarial Robustness through Local Linearization
Chongli Qin, James Martens, Sven Gowal +6
Adversarial training is an effective methodology for training deep neural networks that are robust against adversarial, norm-bounded perturbations. However, the computational cost…
Predicting the Generalization Gap in Deep Networks with Margin Distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi +1
As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functio…
Large Margin Deep Networks for Classification
Gamaleldin F. Elsayed, Dilip Krishnan, Hossein Mobahi +2
We present a formulation of deep learning that aims at producing a large margin classifier. The notion of margin, minimum distance to a decision boundary, has served as the foundat…