papers | Deep Learning JP - Part 82

QUASI-RECURRENT NEURAL NETWORKS

Wasserstein GAN

Towards Principled Methods for Training Generative Adversarial Networks

Exploiting Cyclic Symmetry in Convolutional Neural Networks

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

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