Alternatives and similar repositories for epsilon_free_inference

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

• karlnapf / kernel_hmc
  Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"
  ☆25Updated 6 years ago
• davmre / gprf
  Gaussian Process Random Fields
  ☆21Updated 9 years ago
• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 8 years ago
• emtiyaz / vmp-for-svae
  Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)
  ☆44Updated 7 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
• duvenaud / deep-limits
  Repo for a paper about constructing priors on very deep models.
  ☆73Updated 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
• SheffieldML / deepGPy
  Deep GPs with GPy
  ☆31Updated 9 years ago
• HIPS / maxwells-daemon
  Fastidious accounting of entropy streams into and out of optimization and sampling algorithms.
  ☆32Updated 9 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
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 8 years ago
• thangbui / geepee
  A collection of Gaussian process models
  ☆30Updated 7 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆105Updated 7 years ago
• ebonilla / AutoGP
  Code for AutoGP
  ☆26Updated 5 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆96Updated 7 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆85Updated 3 years ago
• iassael / torch-e2c
  Torch7 impementation of: Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
  ☆42Updated 9 years ago
• wgrathwohl / BackpropThroughTheVoidRL
  RL Experiments from our paper "Backpropagation Through the Void": https://arxiv.org/abs/1711.00123. Lovingly forked from OpenAI's RL Base…
  ☆38Updated 7 years ago
• rhaps0dy / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆39Updated 5 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 8 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• siemens / policy_search_bb-alpha
  ☆68Updated 7 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆66Updated 7 years ago
• tbrx / compiled-inference
  Train neural networks to use as SMC and importance sampling proposals
  ☆24Updated 7 years ago
• karlnapf / kameleon-mcmc
  Code for Kernel Adaptive Metropolis-Hastings
  ☆33Updated 10 years ago
• Sohl-Dickstein / LAHMC
  Look Ahead Hamiltonian Monte Carlo
  ☆30Updated 10 years ago
• marcofraccaro / srnn
  Code for "Sequential Neural Models with Stochastic Layers"
  ☆117Updated 8 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆39Updated 6 years ago
• markvdw / GParML
  Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
  ☆43Updated 11 years ago