Alternatives and similar repositories for GPflow-inter-domain:

Users that are interested in GPflow-inter-domain are comparing it to the libraries listed below

• yincheng / GGP
  Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'
  ☆38Updated 6 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆39Updated 8 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• cambridge-mlg / sghmc_dgp
  ☆28Updated 6 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆83Updated 4 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 7 years ago
• tijiang13 / Semi-supervised-VAE
  ☆25Updated 6 years ago
• xiaoshuai09 / Wasserstein-Learning-For-Point-Process
  learning point processes by means of optimal transport and wasserstein distance
  ☆54Updated 7 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆94Updated 7 years ago
• bobchennan / VAE_NBP
  Variational Auto-encoder with Non-parametric Bayesian Prior
  ☆43Updated 7 years ago
• FelixOpolka / Deep-Gaussian-Process
  🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
  ☆27Updated last year
• melihkandemir / atldgp
  Asymmetric Transfer Learning with Deep Gaussian Processes
  ☆18Updated 9 years ago
• otokonoko8 / deep-Bayesian-nonparametrics-papers
  The collection of papers about combining deep learning and Bayesian nonparametrics
  ☆120Updated 5 years ago
• abietti / dpmm
  Variational inference for Dirichlet process mixture models with multinomial mixture components.
  ☆33Updated 11 years ago
• izmailovpavel / TTGP
  ☆26Updated 6 years ago
• otokonoko8 / implicit-variational-inference
  The collection of recent papers about variational inference
  ☆85Updated 5 years ago
• kirthevasank / add-gp-bandits
  Additive Gaussian Process Bandits - version 1.0
  ☆27Updated 8 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 5 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated 6 months ago
• AMLab-Amsterdam / SEVDL_MGP
  Example implementation of the Bayesian neural network in "Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteri…
  ☆30Updated 4 years ago
• RobRomijnders / ssl_graph
  Semi supervised learning on graphs
  ☆35Updated 7 years ago
• jamesmcinerney / vbmm
  Variational inference for Gaussian mixture models
  ☆35Updated 11 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• prasoongoyal / bnp-vae
  ☆11Updated 7 years ago
• YingzhenLi / vae_renyi_divergence
  A tensorflow implementation of VAE training with Renyi divergence
  ☆31Updated 8 years ago
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 6 years ago
• RuiShu / cvae
  Conditional variational autoencoder implementation in Torch
  ☆105Updated 9 years ago