skolouri / swgmmLinks
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
☆66Updated 2 years ago
Alternatives and similar repositories for swgmm
Users that are interested in swgmm are comparing it to the libraries listed below
Sorting:
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 7 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆58Updated 7 years ago
- Implementation of the Sliced Wasserstein Autoencoders☆91Updated 7 years ago
- ☆53Updated 7 years ago
- Gaussian Process Prior Variational Autoencoder☆87Updated 7 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- Code for Sliced Gromov-Wasserstein☆69Updated 6 years ago
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆29Updated 6 years ago
- Deep convolutional gaussian processes.☆82Updated 6 years ago
- Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)☆20Updated 7 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆101Updated 7 years ago
- Gabriel Peyré, Marco Cuturi, Justin Solomon, Gromov-Wasserstein Averaging of Kernel and Distance Matrices, Proc. of ICML 2016.☆75Updated 9 years ago
- Low-variance, efficient and unbiased gradient estimation for optimizing models with binary latent variables. (ICLR 2019)☆27Updated 6 years ago
- tensorflow implementation of the Wasserstein (aka optimal transport) distance☆73Updated 4 years ago
- Learning generative models with Sinkhorn Loss☆30Updated 7 years ago
- Original PyTorch implementation of Uncertainty-guided Continual Learning with Bayesian Neural Networks, ICLR 2020☆76Updated 4 years ago
- Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…☆32Updated 5 years ago
- Implementation of the MMD VAE paper (InfoVAE: Information Maximizing Variational Autoencoders) in pytorch☆43Updated 5 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 6 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆42Updated 3 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- MMD, Hausdorff and Sinkhorn divergences scaled up to 1,000,000 samples.☆57Updated 6 years ago
- Implementation of Information Dropout☆39Updated 8 years ago
- In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represen…☆41Updated 4 years ago
- ☆66Updated 6 years ago
- Sliced Wasserstein Generator☆22Updated 7 years ago
- Variational Autoencoder with Spatial Broadcast Decoder☆35Updated 6 years ago
- ☆42Updated 5 years ago
- This project is the Torch implementation of our accepted AAAI 2018 paper : orthogonal weight normalization method for solving orthogonali…☆57Updated 6 years ago