All Projects → msurtsukov → Neural Ode

msurtsukov / Neural Ode

Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations

Projects that are alternatives of or similar to Neural Ode

Generative Models
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (+30.75%)
Mutual labels:  jupyter-notebook, vae
Vae protein function
Protein function prediction using a variational autoencoder
Stars: ✭ 57 (-82.99%)
Mutual labels:  jupyter-notebook, vae
Deeplearningmugenknock
でぃーぷらーにんぐを無限にやってディープラーニングでDeepLearningするための実装CheatSheet
Stars: ✭ 684 (+104.18%)
Mutual labels:  jupyter-notebook, vae
Variational Autoencoder
PyTorch implementation of "Auto-Encoding Variational Bayes"
Stars: ✭ 25 (-92.54%)
Mutual labels:  jupyter-notebook, vae
Pytorch Vae
A CNN Variational Autoencoder (CNN-VAE) implemented in PyTorch
Stars: ✭ 181 (-45.97%)
Mutual labels:  jupyter-notebook, vae
Joint Vae
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
Stars: ✭ 404 (+20.6%)
Mutual labels:  jupyter-notebook, vae
Pytorch Mnist Vae
Stars: ✭ 32 (-90.45%)
Mutual labels:  jupyter-notebook, vae
Dsprites Dataset
Dataset to assess the disentanglement properties of unsupervised learning methods
Stars: ✭ 340 (+1.49%)
Mutual labels:  jupyter-notebook, vae
Deep Learning With Python
Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.
Stars: ✭ 134 (-60%)
Mutual labels:  jupyter-notebook, vae
Vae Tensorflow
A Tensorflow implementation of a Variational Autoencoder for the deep learning course at the University of Southern California (USC).
Stars: ✭ 117 (-65.07%)
Mutual labels:  jupyter-notebook, vae
Cross Lingual Voice Cloning
Tacotron 2 - PyTorch implementation with faster-than-realtime inference modified to enable cross lingual voice cloning.
Stars: ✭ 106 (-68.36%)
Mutual labels:  jupyter-notebook, vae
Tf Vqvae
Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
Stars: ✭ 226 (-32.54%)
Mutual labels:  jupyter-notebook, vae
Pytorch Vq Vae
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Stars: ✭ 204 (-39.1%)
Mutual labels:  jupyter-notebook, vae
Human body prior
VPoser: Variational Human Pose Prior
Stars: ✭ 244 (-27.16%)
Mutual labels:  jupyter-notebook, vae
Learning
The data is the future of oil, digging the potential value of the data is very meaningful. This library records my road of machine learning study.
Stars: ✭ 330 (-1.49%)
Mutual labels:  jupyter-notebook
Music Synthesizer For Android
Automatically exported from code.google.com/p/music-synthesizer-for-android
Stars: ✭ 332 (-0.9%)
Mutual labels:  jupyter-notebook
Pytorch notebooks
tutorial notebooks
Stars: ✭ 330 (-1.49%)
Mutual labels:  jupyter-notebook
Automold Road Augmentation Library
This library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific weather and road conditions.
Stars: ✭ 329 (-1.79%)
Mutual labels:  jupyter-notebook
Supervisely
AI for everyone! 🎉 Neural networks, tools and a library we use in Supervisely
Stars: ✭ 332 (-0.9%)
Mutual labels:  jupyter-notebook
Tts
🤖 💬 Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
Stars: ✭ 5,427 (+1520%)
Mutual labels:  jupyter-notebook

Neural ODEs

Notebook here collects theory, basic implementation and some experiments of Neural Ordinary Differential Equations [1].

Link to the blog post
Link to the blog post (Russian)

For actual usage consider using authors original implementation

dyn_func

homotopy

References

[1] Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud. "Neural Ordinary Differential Equations." Advances in Neural Processing Information Systems. 2018.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].