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 and kriging for large scale data

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

A package for Multiple Kernel Learning in Python

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

ML4Chem: Machine Learning for Chemistry and Materials

A Matlab benchmarking toolbox for kernel adaptive filtering

[IEEE TCYB 2022] Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

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

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.

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

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