Alternatives and similar repositories for sghmc

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

• FelixOpolka / Deep-Gaussian-Process
  🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
  ☆25Updated last year
• aboustati / dgplib
  Library for Deep Gaussian Processes based on GPflow
  ☆18Updated 5 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆112Updated 4 years ago
• yucenli / bnn-bo
  Bayesian Neural Network Surrogates for Bayesian Optimization
  ☆67Updated last year
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆74Updated 5 years ago
• benavoli / SkewGP
  Skew Gaussian Processes by Alessio Benavoli, Dario Azzimonti and Dario Piga
  ☆16Updated 5 months ago
• ludwigwinkler / pytorch_MCMC
  Lightweight MCMC sampling for PyTorch Models aka My Corona Project
  ☆52Updated 5 months ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆25Updated 3 years ago
• martinjankowiak / saasbo
  SAASBO: a package for high-dimensional bayesian optimization
  ☆51Updated 4 years ago
• chris-nemeth / sgmcmc-review-paper
  The code in this repository follows the paper "Stochastic gradient MCMC"
  ☆26Updated 6 years ago
• pmorenoz / HetMOGP
  Heterogeneous Multi-output Gaussian Processes
  ☆54Updated 5 years ago
• treforevans / uci_datasets
  Regression datasets from the UCI repository with standardized test-train splits.
  ☆52Updated 3 years ago
• sremes / nonstationary-spectral-kernels
  Implementation for Non-stationary Spectral Kernels (NIPS 2017)
  ☆20Updated 6 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆44Updated last year
• gianlucadetommaso / Stein-variational-samplers
  Python and MATLAB code for Stein Variational sampling methods
  ☆27Updated 6 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 7 years ago
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆47Updated 3 years ago
• markvdw / RobustGP
  Robust initialisation of inducing points in sparse variational GP regression models.
  ☆34Updated 3 years ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆128Updated last year
• ae-foster / dad
  Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
  ☆39Updated 4 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• TyXe-BDL / TyXe
  ☆156Updated 3 years ago
• long-da / A-United-Framework-to-Integrate-Physics-into-Gaussian-Processes
  ☆11Updated 2 years ago
• edwisdom / bnn-hmc
  Bayesian neural nets trained with Hamiltonian Monte Carlo
  ☆10Updated 7 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆236Updated 4 years ago
• ialong / GPt
  Gaussian Processes for Sequential Data
  ☆19Updated 5 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆60Updated 4 years ago
• pmorenoz / ContinualGP
  Continual Gaussian Processes
  ☆31Updated 2 years ago
• cambridge-mlg / sghmc_dgp
  ☆29Updated 6 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆66Updated 6 years ago