shreyas253 / variational_NP_BMMLinks
The source code is related to our work- Shreyas Seshadri, Ulpu Remes and Okko Rasanen: "Dirichlet process mixture models for clustering i-vector data" and "Comparison of Non-parametric Bayesian Mixture Models for Zero-Resource Speech Processing", submitted.
☆10Updated 7 years ago
Alternatives and similar repositories for variational_NP_BMM
Users that are interested in variational_NP_BMM are comparing it to the libraries listed below
Sorting:
- Variational Dirichlet Process Gaussian Mixture Models☆29Updated 10 years ago
- Code for ICML 2019 paper on "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations"☆18Updated 4 years ago
- ☆46Updated 2 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- ☆28Updated 6 years ago
- Variational inference in Dirichlet process Gaussian mixture model (tensorflow implementation)☆13Updated 6 years ago
- Variational Inference in Gaussian Mixture Model☆59Updated 4 years ago
- Matlab Code for Variational Gaussian Copula Inference☆16Updated 9 years ago
- A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…☆54Updated 9 months ago
- Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)☆20Updated 6 years ago
- Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neura…☆40Updated 2 years ago
- Efficient Wasserstein Barycenter in MATLAB (for "Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support" …☆25Updated 5 years ago
- Gaussian Process Prior Variational Autoencoder☆84Updated 6 years ago
- Variational Gaussian Process State-Space Models☆24Updated 9 years ago
- Python implementation of the PR-SSM.☆51Updated 7 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 6 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆121Updated 5 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- Sliced Wasserstein Distance for Learning Gaussian Mixture Models☆63Updated 2 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Kalman Variational Auto-Encoder☆136Updated 6 years ago
- Stochastic Optimization for Optimal Transport☆22Updated 8 years ago
- Heterogeneous Multi-output Gaussian Processes☆52Updated 5 years ago
- ☆40Updated 6 years ago
- Infinite Gaussian Mixture Model / Variational EM☆12Updated 11 years ago
- Python library for Recurrent Gaussian Processes☆19Updated 7 years ago
- Variational inference for Dirichlet process mixture models with multinomial mixture components.☆35Updated 11 years ago
- This is the code for our paper: Semi-Supervised Learning With GANs: Revisiting Manifold Regularization (ICLR 2018)☆44Updated 6 years ago
- This is a re-implementation and test on paper Deep Kalman Filter: https://arxiv.org/pdf/1511.05121.pdf☆19Updated 5 years ago
- Library for Auto-Encoding Sequential Monte Carlo☆18Updated last year