albietz / dpmm

Related projects: ⓘ

• bobchennan / VAE_NBP
  Variational Auto-encoder with Non-parametric Bayesian Prior
  ☆42Updated 7 years ago
• enalisnick / mixture_density_VAEs
  Variational Autoencoders with Gaussian Mixture Latent Space
  ☆36Updated 7 years ago
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆64Updated 4 years ago
• zhegan27 / Bayesian_RNN
  The code for the ACL 2017 paper "Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling"
  ☆29Updated 7 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆38Updated 7 years ago
• prasoongoyal / bnp-vae
  ☆11Updated 6 years ago
• lee813 / pydpmm
  Direct Gibbs sampling for DPMM using python.
  ☆16Updated 7 years ago
• zhegan27 / dpfa_icml2015
  The Matlab Code for the ICML 2015 paper "Scalable Deep Poisson Factor Analysis for Topic Modeling"
  ☆19Updated 8 years ago
• likang7 / variational-dpgmm
  Variational Dirichlet Process Gaussian Mixture Models
  ☆27Updated 9 years ago
• markvdw / GPflow-inter-domain
  Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables
  ☆13Updated 7 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated last year
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆52Updated 3 months ago
• mcusi / tf_dpgmm
  Variational inference in Dirichlet process Gaussian mixture model (tensorflow implementation)
  ☆12Updated 5 years ago
• shaobohan / VariationalGaussianCopula
  Matlab Code for Variational Gaussian Copula Inference
  ☆16Updated 8 years ago
• enalisnick / stick-breaking_dgms
  Deep Generative Models with Stick-Breaking Priors
  ☆94Updated 8 years ago
• jamesmcinerney / vbmm
  Variational inference for Gaussian mixture models
  ☆34Updated 10 years ago
• msobroza / SparsemaxPytorch
  SparseMax activation function implementation (ICML 2016) (PyTorch)
  ☆26Updated 6 years ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 3 years ago
• tijiang13 / Semi-supervised-VAE
  ☆25Updated 5 years ago
• jacobeisenstein / DPMM
  Dirichlet process mixture model code in Matlab. Sampling and variational.
  ☆69Updated 11 years ago
• markvdw / GParML
  Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
  ☆43Updated 10 years ago
• chrisdxie / in-depth-VI-tutorial
  ☆11Updated 8 years ago
• boschresearch / PR-SSM
  Python implementation of the PR-SSM.
  ☆51Updated 6 years ago
• bertini36 / GMM
  Variational Inference in Gaussian Mixture Model
  ☆58Updated 3 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆36Updated 7 years ago
• daeilkim / bbvi
  Black Box Variational Inference
  ☆14Updated 9 years ago
• AMLab-Amsterdam / SEVDL_MGP
  Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…
  ☆31Updated 4 years ago
• sordonia / zforcing
  ZForcing Repo
  ☆40Updated 6 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆74Updated 4 years ago
• RuiShu / cvae
  Conditional variational autoencoder implementation in Torch
  ☆103Updated 8 years ago