Alternatives and similar repositories for atldgp

Users that are interested in atldgp are comparing it to the libraries listed below

• markvdw / GParML
  Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
  ☆43Updated 11 years ago
• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 7 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 7 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• ChunyuanLI / pSGLD
  AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)
  ☆35Updated 6 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 8 years 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
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 7 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆92Updated 3 years ago
• cambridge-mlg / sghmc_dgp
  ☆28Updated 6 years ago
• muupan / deep-ensemble-uncertainty
  An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)
  ☆34Updated 8 years ago
• YingzhenLi / Dropout_BBalpha
  Implementations of the ICML 2017 paper (with Yarin Gal)
  ☆38Updated 7 years ago
• siemens / policy_search_bb-alpha
  ☆68Updated 6 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
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆65Updated 4 years ago
• boschresearch / PR-SSM
  Python implementation of the PR-SSM.
  ☆51Updated 6 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• YingzhenLi / stochastic-EP
  code for stochastic expectation propagation
  ☆16Updated 9 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆66Updated 7 years ago
• kirthevasank / add-gp-bandits
  Additive Gaussian Process Bandits - version 1.0
  ☆27Updated 8 years ago
• markvdw / GPflow-inter-domain
  Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables
  ☆13Updated 7 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• YingzhenLi / vae_renyi_divergence
  A tensorflow implementation of VAE training with Renyi divergence
  ☆31Updated 8 years ago
• TENGBINN / Bayes-by-Backprop
  TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper
  ☆51Updated 6 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• BigBayes / SGRLD
  Stochastic Gradient Riemannian Langevin Dynamics
  ☆33Updated 9 years ago