Deep Learning

1. Maxout Networks
2. Adam- A Method for Stochastic Optimization
3. An empirical analysis of dropout in piecewise linear networks
4. FitNets- Hints for Thin Deep Nets
5. Deep Forest- Towards An Alternative to Deep Neural Networks
6. Deep Neural Networks are Easily Fooled
7. Explaining and Harnessing Adversarial Examples
8. Understanding Deep Learning Requires Rethinking Generalization
9. SeqGAN- Sequence Generative Adversarial Nets with Policy Gradient
10. Improved Training of Wasserstein GANs
11. From Image-level to Pixel-level Labeling with Convolutional Networks
12. “Why Should I Trust You?” Explaining the Predictions of Any Classifier
13. Distributional smoothing by virtual adversarial examples.