Alternatives and similar repositories for pykernels

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

• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆251Updated last year
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 8 years ago
• josejimenezluna / pyGPGO
  Bayesian optimization for Python
  ☆246Updated 3 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
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• bnpy / bnpy
  Bayesian nonparametric machine learning for Python
  ☆233Updated 2 years ago
• jlsuarezdiaz / pyDML
  Distance Metric Learning Algorithms for Python
  ☆175Updated 4 years ago
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 7 years ago
• slinderman / pypolyagamma
  Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
  ☆83Updated 5 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆110Updated 7 years ago
• nicola-decao / s-vae-tf
  Tensorflow implementation of Hyperspherical Variational Auto-Encoders
  ☆237Updated 7 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆85Updated 4 years ago
• msmbuilder / vde
  Variational Autoencoder for Dimensionality Reduction of Time-Series
  ☆189Updated 3 years ago
• duvenaud / warped-mixtures
  Code for density estimation with nonparametric cluster shapes.
  ☆39Updated 9 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆64Updated 6 years ago
• marionmari / pyGPs
  pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.
  ☆217Updated 6 years ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆147Updated last year
• ctallec / pyvarinf
  Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
  ☆361Updated 6 years ago
• oxcsml / kerpy
  python code for kernel methods
  ☆41Updated 7 years ago
• cangermueller / ppca
  Probabilistic Principal Component Analysis
  ☆64Updated 8 years ago
• SheffieldML / vargplvm
  Bayesian GPLVM in MATLAB and R
  ☆76Updated 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
• izmailovpavel / TTGP
  ☆25Updated 7 years ago
• mmp2 / megaman
  megaman: Manifold Learning for Millions of Points
  ☆332Updated 2 years ago
• t-vi / candlegp
  Gaussian Processes in Pytorch
  ☆75Updated 5 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆83Updated 6 years ago
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆184Updated 6 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 6 years ago