Alternatives and similar repositories for cse515t

Users that are interested in cse515t are comparing it to the libraries listed below

• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆67Updated 8 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆110Updated 7 years ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆169Updated 2 years ago
• oxcsml / ML_bazaar
  OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.
  ☆94Updated 4 years ago
• thangbui / geepee
  A collection of Gaussian process models
  ☆30Updated 8 years ago
• mlss-2019 / tutorials
  ☆276Updated 5 years ago
• inf16nyu / home
  ☆78Updated 8 years ago
• brain-research / l2hmc
  TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
  ☆183Updated 7 years ago
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 8 years ago
• duvenaud / phd-thesis
  I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth
  ☆188Updated 11 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• lawrennd / old_talks
  Talks from Neil Lawrence
  ☆54Updated 2 years ago
• SheffieldML / vargplvm
  Bayesian GPLVM in MATLAB and R
  ☆76Updated 8 years ago
• slinderman / pypolyagamma
  Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
  ☆83Updated 5 years ago
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 9 years ago
• dustinvtran / bayesrl
  A Python library for reinforcement learning using Bayesian approaches
  ☆53Updated 10 years ago
• duvenaud / deep-limits
  Repo for a paper about constructing priors on very deep models.
  ☆73Updated 9 years ago
• joanbruna / ir18
  Inference and Representation (DS-GA-1005, CSCI-GA.2569), fall 18
  ☆69Updated 6 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
• jameshensman / VFF
  Variational Fourier Features
  ☆85Updated 4 years ago
• fabianp / overview_optalgs
  ☆30Updated 5 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆93Updated 4 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
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆251Updated last year
• wiseodd / probabilistic-models
  Collection of probabilistic models and inference algorithms
  ☆240Updated 5 years ago
• andymiller / ReducedVarianceReparamGradients
  Reducing Reparameterization Gradient Variance code.
  ☆33Updated 8 years ago
• mattjj / pyslds
  ☆98Updated 7 years ago
• jameshensman / GPclust
  Clustering time series using Gaussian processes and Variational Bayes.
  ☆39Updated 5 years ago