Alternatives and similar repositories for sgld

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

• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆83Updated last year
• wjmaddox / spectralgp
  Code repo for "Function-Space Distributions over Kernels"
  ☆32Updated 4 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆112Updated 4 years ago
• ganguli-lab / deepchaos
  Experiments for the paper "Exponential expressivity in deep neural networks through transient chaos"
  ☆74Updated 9 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆101Updated 7 years ago
• google-deepmind / mc_gradients
  ☆172Updated last year
• amirgholami / anode
  [IJCAI'19, NeurIPS'19] Anode: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
  ☆108Updated 5 years ago
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 7 years ago
• MichaelArbel / GeneralizedEBM
  ☆54Updated last year
• ericjang / nf-jax
  Normalizing Flows in Jax
  ☆107Updated 5 years ago
• duvenaud / learn-discrete
  Graduate topics course on learning discrete latent structure.
  ☆67Updated 6 years ago
• conormdurkan / autoregressive-energy-machines
  Autoregressive Energy Machines
  ☆78Updated 3 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• thjashin / solvegp
  Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)
  ☆22Updated 4 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 4 years ago
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆132Updated 6 years ago
• ermongroup / higher_order_invariance
  Code for "Accelerating Natural Gradient with Higher-Order Invariance"
  ☆30Updated 6 years ago
• MFreidank / pysgmcmc
  Bayesian Deep Learning with Stochastic Gradient MCMC Methods
  ☆38Updated 4 years ago
• ChunyuanLI / SVGD
  Tensorflow implementation of Stein Variational Gradient Descent (SVGD)
  ☆26Updated 7 years ago
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 5 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 7 years ago
• ruqizhang / csgmcmc
  Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
  ☆93Updated 5 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 6 years ago
• fKunstner / limitations-empirical-fisher
  Limitations of the Empirical Fisher Approximation
  ☆49Updated 9 months ago
• g-benton / learning-invariances
  Codebase for Learning Invariances in Neural Networks
  ☆96Updated 3 years ago
• lxuechen / BDMC
  PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
  ☆52Updated 4 years ago
• blei-lab / variational-smc
  Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)
  ☆65Updated 6 years ago
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated 2 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆83Updated 6 years ago