Alternatives and similar repositories for pykernels

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

• josejimenezluna / pyGPGO
  Bayesian optimization for Python
  ☆246Updated 3 years ago
• alshedivat / keras-gp
  Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
  ☆249Updated last year
• yhenon / pyrcc
  Python implementation of Robust Continuous Clustering
  ☆106Updated 6 years ago
• mmp2 / megaman
  megaman: Manifold Learning for Millions of Points
  ☆329Updated 2 years ago
• bnpy / bnpy
  Bayesian nonparametric machine learning for Python
  ☆228Updated 2 years ago
• lawrennd / old_talks
  Talks from Neil Lawrence
  ☆54Updated last year
• markvdw / convgp
  Convolutional Gaussian processes based on GPflow.
  ☆95Updated 7 years ago
• jlsuarezdiaz / pyDML
  Distance Metric Learning Algorithms for Python
  ☆173Updated 4 years ago
• jameshensman / VFF
  Variational Fourier Features
  ☆85Updated 4 years ago
• ctallec / pyvarinf
  Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
  ☆361Updated 5 years ago
• mstrazar / mklaren
  A Python Multiple kernel learning library.
  ☆57Updated 6 years ago
• PGM-Lab / InferPy
  InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy
  ☆148Updated last year
• msmbuilder / vde
  Variational Autoencoder for Dimensionality Reduction of Time-Series
  ☆189Updated 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
• mauriziofilippone / deep_gp_random_features
  ☆40Updated 6 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆94Updated 7 years ago
• wittawatj / interpretable-test
  NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
  ☆64Updated 7 years ago
• rflamary / OTML_Statlearn2018
  Courses and practical sessions for the Optimal Transport and Machine learning course at Statlearn 2018
  ☆26Updated 7 years ago
• stephenhky / PyTDA
  Topological Data Analysis in Python
  ☆170Updated 6 years ago
• oxcsml / kerpy
  python code for kernel methods
  ☆41Updated 6 years ago
• BiuBiuBiLL / NPEET_LNC
  local non-uniformity correction for mutual information estimation
  ☆70Updated 4 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆151Updated 6 years ago
• drckf / paysage
  Unsupervised learning and generative models in python/pytorch.
  ☆119Updated 2 years ago
• jmetzen / gp_extras
  Additional kernels that can be used with scikit-learn's Gaussian Process module
  ☆82Updated last year
• pfnet-research / bayesgrad
  BayesGrad: Explaining Predictions of Graph Convolutional Networks
  ☆63Updated 3 years ago
• skggm / skggm
  Scikit-learn compatible estimation of general graphical models
  ☆248Updated 3 months ago
• nicola-decao / s-vae-tf
  Tensorflow implementation of Hyperspherical Variational Auto-Encoders
  ☆234Updated 6 years ago
• slinderman / pypolyagamma
  Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.
  ☆83Updated 5 years ago
• edwardlib / observations
  Tools for loading standard data sets in machine learning
  ☆204Updated 2 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆64Updated 6 years ago