| .. | ||
| assets | ||
| __init__.py | ||
| draw.md | ||
| GAN.md | ||
| pixelrnn.md | ||
| README.md | ||
| rnnrbm.md | ||
| styletransfer.md | ||
| summary_generation_sequences.md | ||
This section of our repository holds reviews of research papers that we think everyone in the field should read and understand. It currently includes:
- DRAW: A Recurrent Neural Network For Image Generation by Gregor et al. (Review by Tim Cooijmans)
- Generating Sequences with Recurrent Neural Networks by Graves. (Review by David Ha)
- A Neural Algorithm of Artistic Style by Gatys et al. (Review by Cinjon Resnick)
- Pixel Recurrent Neural Networks by Van den Oord et al. (Review by Kyle Kastner)
- Generative Adversarial Networks by Goodfellow et al. (Review by Max Strakhov)
- Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription by Boulanger-Lewandowski et al. (Review by Dan Shiebler)
There are certainly many other papers and resources that belong here. We want this to be a community endeavor and encourage high-quality summaries, both in terms of reviews and selection. So if you have a favorite, please file an issue saying which paper you want to write about. After we approve the topic, submit a pull request and we’ll be delighted to showcase your work.