Alternatives and similar repositories for automl-phase-1:

Users that are interested in automl-phase-1 are comparing it to the libraries listed below

• jamesrobertlloyd / automl-phase-2
  1st place submission to the AutoML competition - phase 2
  ☆28Updated 9 years ago
• SheffieldML / collab
  Collaborative filtering with the GP-LVM
  ☆25Updated 9 years ago
• fabianp / hoag
  Hyperparameter optimization with approximate gradient
  ☆66Updated 3 years ago
• duvenaud / deep-limits
  Repo for a paper about constructing priors on very deep models.
  ☆72Updated 8 years ago
• blei-lab / deep-exponential-families
  Deep exponential families (DEFs)
  ☆55Updated 7 years ago
• gngdb / variational-dropout
  Replication of the paper "Variational Dropout and the Local Reparameterization Trick" using Lasagne.
  ☆33Updated 7 years ago
• YingzhenLi / stochastic-EP
  code for stochastic expectation propagation
  ☆16Updated 9 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 5 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆83Updated 3 years ago
• automl / pylearningcurvepredictor
  predicting learning curves in python
  ☆43Updated 7 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆65Updated 7 years ago
• RuiShu / nn-bayesian-optimization
  We use a modified neural network instead of Gaussian process for Bayesian optimization.
  ☆108Updated 7 years ago
• markvdw / GParML
  Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
  ☆43Updated 11 years ago
• pmineiro / randembed
  Randomized embeddings for extreme learning
  ☆24Updated 9 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 7 years ago
• gregversteeg / discrete_sieve
  The information sieve for discrete variables.
  ☆36Updated 8 years ago
• ChunyuanLI / pSGLD
  AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)
  ☆35Updated 6 years ago
• kzhai / PyNPB
  Non-parametric Bayesian in Python, including Indian buffet process (IBP), hierarchical Dirichlet process (HDP).
  ☆73Updated 9 years ago
• arahuja / hamiltonian-monte-carlo
  Implementation of Hamiltonian Monte Carlo using Google's TensorFlow
  ☆47Updated 9 years ago
• TENGBINN / Bayes-by-Backprop
  TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper
  ☆51Updated 6 years ago
• zhegan27 / dpfa_icml2015
  The Matlab Code for the ICML 2015 paper "Scalable Deep Poisson Factor Analysis for Topic Modeling"
  ☆19Updated 9 years ago
• larsmaaloee / auxiliary-deep-generative-models
  Deep generative models for semi-supervised learning.
  ☆108Updated 8 years ago
• YingzhenLi / Dropout_BBalpha
  Implementations of the ICML 2017 paper (with Yarin Gal)
  ☆38Updated 7 years ago
• mrkulk / SequentialInference
  Dirichlet Process Mixture using PVI, SMC, Variational
  ☆15Updated 10 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 5 years ago
• mwhoffman / pybo
  Python package for modular Bayesian optimization
  ☆134Updated 4 years ago
• ofirnachum / uncertainty_gbm
  Sklearn implementation of GBM to predict mu(X) and std(X) on heteroscedastic data
  ☆26Updated 8 years ago
• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 7 years ago
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• hyperopt / hyperopt-nnet
  Hyperparameter optimization for neural networks
  ☆47Updated 11 years ago