{"id":20714664,"url":"https://github.com/fabsta/vae_playground","last_synced_at":"2025-03-11T06:47:20.997Z","repository":{"id":142929817,"uuid":"158514224","full_name":"fabsta/vae_playground","owner":"fabsta","description":null,"archived":false,"fork":false,"pushed_at":"2018-11-26T09:39:39.000Z","size":50979,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-17T21:43:34.767Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# vae_playground\n\n### molecule generator autoencoder\n\nInteresting papers/codes:\n\n* 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)  \n* 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)\n\nStarting points:  \n\nKeras \n[Autoencoder in keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n\nMNIST:  [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)  \n[Convolutional autoencoder (exercise/solution)](https://github.com/udacity/deep-learning-v2-pytorch/tree/master/autoencoder/convolutional-autoencoder)  \n[Denoising autoencoder (exercise/solution)](https://github.com/udacity/deep-learning-v2-pytorch/tree/master/autoencoder/denoising-autoencoder)\n\npytorch/VAE  \nGraph decoders  [DeepChem issue](https://github.com/deepchem/deepchem/issues/1097),  [github jt-vae](https://github.com/lilleswing/jt-vae)  \n[github icml18-jtnn](https://github.com/lilleswing/icml18-jtnn),  [github 2](https://github.com/wengong-jin/icml18-jtnn)\n\n-   [Movie recommendation example](http://nbviewer.jupyter.org/github/lightsalsa251/Movie-Recommender-System/blob/master/Recommendation%20System.ipynb)\n    \n-   [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))\n    \n-   [Awesome pytorch list](https://github.com/bharathgs/Awesome-pytorch-list)\n    \n\nYoutube:\n\n-   [Syntax Directed Variational Autoencoders and Other Methods of Drug Discovery](https://www.youtube.com/watch?v=6HqIk-PxpCo\u0026t=2s),  [slides](https://docs.google.com/presentation/d/1acNguM5q2Y0clg74dxFpnP3HMT9HhD6ayzhOnmbLmv4/edit)\n-   [TDLS: Junction Tree Variational Autoencoder for Molecular Graph Generation](https://www.youtube.com/watch?v=QFRv_lOWeKI)\n-   [Grammar Variational Autoencoder](https://youtu.be/ar4Fm1V65Fw?t=241)\n\n[Overview of pytorch autoencoders](https://medium.com/@s.ganjoo96/autoencoders-with-pytorch-a89ed28f94a9)\n\nTheory  \n[VAE explained](http://kvfrans.com/variational-autoencoders-explained/)\n\u003c!--stackedit_data:\neyJoaXN0b3J5IjpbLTk2ODU5MzMwNiw4ODYxOTU4MzQsNzg1Mj\nQ0MTc2LDk5NjcwMDkxNSwxMzQ0NDkwNTExXX0=\n--\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabsta%2Fvae_playground","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffabsta%2Fvae_playground","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffabsta%2Fvae_playground/lists"}