mcusi / tf_dpgmmLinks
Variational inference in Dirichlet process Gaussian mixture model (tensorflow implementation)
☆13Updated 7 years ago
Alternatives and similar repositories for tf_dpgmm
Users that are interested in tf_dpgmm are comparing it to the libraries listed below
Sorting:
- Variational Autoencoders with Gaussian Mixture Latent Space☆36Updated 8 years ago
- Python implementation of the PR-SSM.☆56Updated 7 years ago
- Gaussian Process Prior Variational Autoencoder☆86Updated 6 years ago
- Implementation of mutual learning model between VAE and GMM.☆28Updated last month
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- ☆19Updated 3 years ago
- Implementation of the Neural Clustering Process algorithm in Pytorch☆31Updated 5 years ago
- Variational inference for Dirichlet process mixture models with multinomial mixture components.☆35Updated 11 years ago
- PyTorch implementation of the Covariate-GPLVM☆25Updated 5 years ago
- Code for "oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis"☆26Updated 5 years ago
- pytorch implementation of https://arxiv.org/abs/1705.09847☆15Updated 5 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 2 years ago
- Source code for "When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian …☆10Updated 4 years ago
- Classifier based mutual information, conditional mutual information estimation; conditional independence testing☆35Updated 6 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 5 years ago
- Code for "Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models" (ICML 2019)☆45Updated 5 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- Python3 implementation of the paper [Large-scale optimal transport map estimation using projection pursuit]☆15Updated 4 years ago
- Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019☆41Updated 2 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- ☆78Updated 4 years ago
- ☆32Updated 2 years ago
- Spatio-temporal alignements: Optimal transport in space and time☆48Updated 6 months ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 7 years ago
- A PyTorch implementation of a Deep Hidden Markov Model [Structured Inference Networks for Nonlinear State Space Models]☆59Updated last year
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆66Updated 2 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- ☆32Updated 7 years ago
- Kalman Variational Auto-Encoder☆137Updated 6 years ago
- Stochastic algorithms for computing Regularized Optimal Transport☆58Updated 7 years ago