A collection of important graph embedding, classification and representation learning papers with implementations.

Learning kernels to maximize the power of MMD tests

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Scala Library/REPL for Machine Learning Research

Large-scale, multi-GPU capable, kernel solver

Fast radial basis function interpolation for large scale data

A package for Multiple Kernel Learning in Python

A python package for graph kernels, graph edit distances, and graph pre-image problem.

A Matlab benchmarking toolbox for kernel adaptive filtering

ML4Chem: Machine Learning for Chemistry and Materials

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326

NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.

[IEEE TCYB 2021] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.

SPLASH is an interactive visualisation and plotting tool using kernel interpolation, mainly used for Smoothed Particle Hydrodynamics simulations

Kernel Methods Toolbox for Matlab/Octave

Implementation of LMS, RLS, KLMS and KRLS filters in Python

Multivariate Local Polynomial Regression and Radial Basis Function Regression