Alternatives and similar repositories for overview_optalgs:

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

• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 7 years ago
• DanielTakeshi / MCMC_and_Dynamics
  Practice with MCMC methods and dynamics (Langevin, Hamiltonian, etc.)
  ☆42Updated 5 years ago
• suriyadeepan / BayesianML
  Experiments in Bayesian Machine Learning
  ☆69Updated 5 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆66Updated 7 years ago
• duvenaud / deep-limits
  Repo for a paper about constructing priors on very deep models.
  ☆72Updated 8 years ago
• HIPS / maxwells-daemon
  Fastidious accounting of entropy streams into and out of optimization and sampling algorithms.
  ☆32Updated 8 years ago
• jluttine / variational-bayes-book
  Open access book on variational Bayesian methods written collaboratively
  ☆28Updated 9 years ago
• approximateinference / approximateinference.github.io
  NeurIPS workshop on Advances in Approximate Bayesian Inference
  ☆48Updated this week
• rueberger / MJHMC
  Python implementation of Markov Jump Hamiltonian Monte Carlo
  ☆24Updated 8 years ago
• offconvex / offconvex.github.io
  Off the convex path
  ☆67Updated 2 years ago
• casmls / casmls.github.io
  Columbia Advanced Machine Learning Seminar
  ☆24Updated 6 years ago
• DeepLearningDTU / nvidia_deep_learning_summercamp_2016
  Lasagne / Theano tutorials for Nvidia Deep Learning Summercamp 2016
  ☆26Updated 8 years ago
• brendenlake / structural-sparsity
  Structure learning for sparse graphs with latent variables
  ☆45Updated 8 years ago
• Sohl-Dickstein / LAHMC
  Look Ahead Hamiltonian Monte Carlo
  ☆30Updated 9 years ago
• tbroderick / streaming_vb
  This is code associated with the paper: Broderick, T, Boyd, N, Wibisono, A, Wilson, AC, and Jordan, MI. Streaming variational Bayes. Neur…
  ☆41Updated 10 years ago
• mwhoffman / pygp
  Python package for inference with Gaussian processes
  ☆11Updated 9 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 8 years ago
• wittawatj / kernel-mod
  NeurIPS 2018. Linear-time model comparison tests.
  ☆18Updated 5 years ago
• PMBio / pygp
  A Gaussian process toolbox in python
  ☆42Updated 12 years ago
• aschein / bptd
  Bayesian Poisson Tucker decomposition
  ☆17Updated 8 years ago
• ThomasMiconi / BOHP_RNN
  Backprop training of recurrent neural networks with Hebbian plastic connections
  ☆20Updated 3 years ago
• karlnapf / kernel_hmc
  Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"
  ☆25Updated 6 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
• philipphennig / probabilistic-numerics.org
  This repository houses the code for the community website http://www.probabilistic-numerics.org
  ☆35Updated 4 years ago
• locuslab / newton_admm
  A Newton ADMM based solver for Cone programming.
  ☆38Updated 7 years ago
• tbrx / compiled-inference
  Train neural networks to use as SMC and importance sampling proposals
  ☆24Updated 7 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 5 years ago
• yaringal / acquisition_example
  Train a simple convnet on the MNIST dataset and evaluate the BALD acquisition function
  ☆16Updated 7 years ago
• chrisdxie / in-depth-VI-tutorial
  ☆11Updated 8 years ago
• charlesmartin14 / emf-rbm
  Extended Mean Field Restricted Boltzmann Machine
  ☆17Updated 8 years ago