Generative Models

Repository for self-teaching of Generative Models and its applications.

• Generative Models
• How to reference this repo.
• Courses
• Readables and Watchables
  • Introductory
  • Variational Inference
  • Generative Adversarial Networks (GANs)
    • 2019
    • 2016
    • 2014
  • Applications
    • Bioinformatics
    • Healthcare
    • Reinforcement Learning
• Related repos & tutorials.

How to reference this repo.

@misc{Severo2019,
  author = {Severo, D.},
  title = {Generative Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/dsevero/generative-models}},
  commit = {master}
}

Courses

CS231n: Convolutional Neural Networks for Visual Recognition CS 228 - Probabilistic Graphical Models

Readables and Watchables

Introductory

UAI 2017 Tutorial: Shakir Mohamed & Danilo Rezende 🇧🇷

https://openai.com/blog/generative-models/

Stanford - Lecture 13 | Generative Models

Variational Inference

Dustin Tran and Alp Kucukelbir and Adji B. Dieng and Maja Rudolph and Dawen Liang and David M. Blei, Edward: A library for probabilistic modeling, inference, and criticism

Martin J. Wainwright1 and Michael I. Jordan2, Graphical Models, Exponential Families, and Variational Inference

Andrew Miller's Blog

Eric P. Xing, Variational inference II

Eric Lang, A Beginner's Guide to Variational Methods: Mean-Field Approximation

Zhiya Zuo's Blog

Generative Adversarial Networks (GANs)

2019

Odena, "Open Questions about Generative Adversarial Networks", Distill, 2019.

2016

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems (pp. 2234-2242).

Goodfellow, I. (2016). NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160.

Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems (pp. 2172-2180).

Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)

Introduction to GANs, NIPS 2016 | Ian Goodfellow, OpenAI

2014

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

Applications

Bioinformatics

Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., & Frey, B. J. (2017). Generating and designing DNA with deep generative models. arXiv preprint arXiv:1712.06148.

Healthcare

Guan, J., Li, R., Yu, S., & Zhang, X. (2018, December). Generation of Synthetic Electronic Medical Record Text. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 374-380). IEEE.

Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W. F., & Sun, J. (2017). Generating multi-label discrete patient records using generative adversarial networks. arXiv preprint arXiv:1703.06490.

Reinforcement Learning

Yu, L., Zhang, W., Wang, J., & Yu, Y. (2017, February). Seqgan: Sequence generative adversarial nets with policy gradient. In Thirty-First AAAI Conference on Artificial Intelligence.

Related repos & tutorials.

The GAN Zoo

How to Train a GAN? Tips and tricks to make GANs work

https://ceit.aut.ac.ir/~khalooei/tutorials/gan/

CVPR 2018 Tutorial on GANs

Keras implementations of GANs.

PyTorch implementations of GANs