Alternatives and similar repositories for deep-Bayesian-nonparametrics-papers:

Users that are interested in deep-Bayesian-nonparametrics-papers are comparing it to the libraries listed below

• otokonoko8 / implicit-variational-inference
  The collection of recent papers about variational inference
  ☆85Updated 5 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆80Updated 4 years ago
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆131Updated 5 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆110Updated 3 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆64Updated 5 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆81Updated 7 months ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆48Updated 6 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆148Updated 5 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 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
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• ivannz / mlss2019-bayesian-deep-learning
  MLSS2019 Tutorial on Bayesian Deep Learning
  ☆92Updated 5 years ago
• cambridge-mlg / sghmc_dgp
  ☆28Updated 5 years ago
• riannevdberg / sylvester-flows
  ☆178Updated 5 years ago
• blei-lab / ars-reparameterization
  Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)
  ☆39Updated 7 years ago
• oxcsml / ML_bazaar
  OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.
  ☆92Updated 3 years ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 3 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…
  ☆53Updated 3 months ago
• ssydasheng / Bayesian_neural_network_papers
  Papers for Bayesian-NN
  ☆318Updated 5 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆78Updated 5 years ago
• geniki / neural-processes
  PyTorch implementation of Neural Processes
  ☆88Updated 5 years ago
• ex4sperans / variational-inference-with-normalizing-flows
  Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
  ☆230Updated 6 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆38Updated 5 years ago
• freelunchtheorem / Conditional_Density_Estimation
  Package implementing various parametric and nonparametric methods for conditional density estimation
  ☆190Updated last year
• CW-Huang / NAF
  Experiments for the Neural Autoregressive Flows paper
  ☆124Updated 3 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 4 years ago
• thjashin / spectral-stein-grad
  Code for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)
  ☆32Updated last year
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 7 years ago