Summary of Papers on Deep Learning

• Universal Language Model Fine-tuning for Text Classification [Paper], [Slides]

  • Jeremy Howard, Sebastian Ruder, ACL, 2018

• Two Novel Seq2Seq Architectures: ConvS2S [Paper], Transfomer [Paper], [Review]

• Understanding Black-box Predictions via Influence Functions [Paper] [Review]

  • Pang Wei Koh, Percy Liang, ICML, 2017, Best paper award

• Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning [Paper] [Review]

  • Jiasen Lu, Caiming Xiong, Devi Parikh, Richard Socher, CVPR, 2017

• Hierarchical Question-Image Co-Attention for Visual Question Answering [Paper] [Review]

  • Jiasen Lu, Jianwei Yang, Dhruv Batra, Devi Parikh, NIPS, 2016

• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles [Paper][Review]

  • Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David Crandall, Dhruv Batra, NIPS, 2016

• Generative Adversarial Nets [Paper] [Review]

  • Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, NIPS, 2014

• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [Paper] [Review]

  • Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, Dimitris Metaxas, ArXiv, 2016

• Wasserstein GAN [Paper] [Review]

  • Martin Arjovsky, Soumith Chintala, Léon Bottou, ArXiv, 2017

• Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space [Paper] [Review]

  • Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, Jeff Clune, ArXiv, 2016

• Label-Free Supervision of Neural Networks with Physics and Domain Knowledge [Paper] [Review]

  • Russell Stewart, Stefano Ermon, AAAI, 2017, Outstanding paper award

• Quasi-Recurrent Neural Networks [Paper][Review]

  • James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher, ICLR, 2017

• Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models [Paper] [Review]

  • Sergey Ioffe, ArXiv, 2017

• My Overview of Deep Learning for NLP [Review]

Notes of Deep Learning Book

• Chapter 6: Deep Forward Networks [Review]
• Chapter 7: Regularization for Deep Learning [Review]
• Chapter 8: Optimization for Training Deep Models [Review]
• Chapter 9: Convolutional Networks [Review]
• Chapter 10: Sequence Modeling: Reccurent and Recursive Nets [Review]
• Chapter 11: Practical Methodology [Review]
• Chapter 12: Applications [Review]
• Chapter 13: Linear Factor Models [Review]
• Chapter 14: Autoencoders [Review]
• Chapter 15: Representation Learning [Review]
• Chapter 16: Structured Probabilistic Models for Deep Learning [Review]
• Chapter 17: Monte Carlo Methods [Review]
• Chapter 18: Confronting the Partition Function [Review]
• Chapter 19: Approximate Inference [Review]
• Chapter 20: Deep Generative Models [Review]