seminar | Deep Learning JP - Part 84

Understanding deep learning requires rethinking generalization

Stochastic Neural Networks for Hierarchical Reinforcement Learning

Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning

U-Net: Convolutional Networks for Biomedical Image Segmentation (MICCAI 2015)

Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (NIPS 2016 Poster)

Bayesian Deep Learning

Learning What and Where to Draw (NIPS’16)

Dual Learning for Machine Translation

Value Iteration Networks

Image-to-Image Translation with Conditional Adversarial Networks

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