aiexperiments-ai-duet/server/magenta/reviews
2016-11-17 07:33:16 +03:00
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assets Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
__init__.py Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
draw.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
GAN.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
pixelrnn.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
README.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
rnnrbm.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
styletransfer.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00
summary_generation_sequences.md Add Dockerfile; fix predict.py and server.py 2016-11-17 07:33:16 +03:00

This section of our repository holds reviews of research papers that we think everyone in the field should read and understand. It currently includes:

  1. DRAW: A Recurrent Neural Network For Image Generation by Gregor et al. (Review by Tim Cooijmans)
  2. Generating Sequences with Recurrent Neural Networks by Graves. (Review by David Ha)
  3. A Neural Algorithm of Artistic Style by Gatys et al. (Review by Cinjon Resnick)
  4. Pixel Recurrent Neural Networks by Van den Oord et al. (Review by Kyle Kastner)
  5. Generative Adversarial Networks by Goodfellow et al. (Review by Max Strakhov)
  6. 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 well be delighted to showcase your work.