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stat.ML2019
Understanding and Improving Virtual Adversarial Training
Dongha Kim, Yongchan Choi, Yongdai Kim
In semi-supervised learning, virtual adversarial training (VAT) approach is one of the most attractive method due to its intuitional simplicity and powerful performances. VAT finds…
stat.ML2018
On variation of gradients of deep neural networks
Yongdai Kim, Dongha Kim
We provide a theoretical explanation of the role of the number of nodes at each layer in deep neural networks. We prove that the largest variation of a deep neural network with ReL…
stat.ML2018
Fast convergence rates of deep neural networks for classification
Yongdai Kim, Ilsang Ohn, Dongha Kim
We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider t…