https://github.com/fabsta/vae_playground
https://github.com/fabsta/vae_playground
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/fabsta/vae_playground
- Owner: fabsta
- Created: 2018-11-21T08:19:34.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-11-26T09:39:39.000Z (over 7 years ago)
- Last Synced: 2025-01-17T21:43:34.767Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 48.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# vae_playground
### molecule generator autoencoder
Interesting papers/codes:
* CDN_Molecule: [paper](https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.8b00474), [code](https://github.com/rkakamilan/CDN_Molecule), [train function](https://github.com/rkakamilan/CDN_Molecule/blob/master/train.py), [evaluate](https://github.com/rkakamilan/CDN_Molecule/blob/master/evaluate.py)
* chemical_vae: [github code](https://github.com/aspuru-guzik-group/chemical_vae), [example notebook](http://nbviewer.jupyter.org/github/fabsta/vae_playground/blob/master/chemical_vae/examples/intro_to_chemvae.ipynb)
Starting points:
Keras
[Autoencoder in keras](https://blog.keras.io/building-autoencoders-in-keras.html)
MNIST: [Pytorch VAE example](https://github.com/pytorch/examples/tree/master/vae), [another](http://nbviewer.jupyter.org/github/nitarshan/variational-autoencoder/blob/master/Variational%20Autoencoder%20Tutorial.ipynb), [more detailled](http://nbviewer.jupyter.org/github/nitarshan/variational-autoencoder/blob/master/Variational%20Autoencoder%20Tutorial.ipynb)
[Convolutional autoencoder (exercise/solution)](https://github.com/udacity/deep-learning-v2-pytorch/tree/master/autoencoder/convolutional-autoencoder)
[Denoising autoencoder (exercise/solution)](https://github.com/udacity/deep-learning-v2-pytorch/tree/master/autoencoder/denoising-autoencoder)
pytorch/VAE
Graph decoders [DeepChem issue](https://github.com/deepchem/deepchem/issues/1097), [github jt-vae](https://github.com/lilleswing/jt-vae)
[github icml18-jtnn](https://github.com/lilleswing/icml18-jtnn), [github 2](https://github.com/wengong-jin/icml18-jtnn)
- [Movie recommendation example](http://nbviewer.jupyter.org/github/lightsalsa251/Movie-Recommender-System/blob/master/Recommendation%20System.ipynb)
- [General deep learning experiments](https://github.com/tfolkman/deep-learning-experiments) (e.g.: [VAE example](http://nbviewer.jupyter.org/github/tfolkman/deep-learning-experiments/blob/master/VAE.ipynb))
- [Awesome pytorch list](https://github.com/bharathgs/Awesome-pytorch-list)
Youtube:
- [Syntax Directed Variational Autoencoders and Other Methods of Drug Discovery](https://www.youtube.com/watch?v=6HqIk-PxpCo&t=2s), [slides](https://docs.google.com/presentation/d/1acNguM5q2Y0clg74dxFpnP3HMT9HhD6ayzhOnmbLmv4/edit)
- [TDLS: Junction Tree Variational Autoencoder for Molecular Graph Generation](https://www.youtube.com/watch?v=QFRv_lOWeKI)
- [Grammar Variational Autoencoder](https://youtu.be/ar4Fm1V65Fw?t=241)
[Overview of pytorch autoencoders](https://medium.com/@s.ganjoo96/autoencoders-with-pytorch-a89ed28f94a9)
Theory
[VAE explained](http://kvfrans.com/variational-autoencoders-explained/)