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

Fast radial basis function interpolation and kriging 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

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

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

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

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

This is the page for the book Digital Signal Processing with Kernel Methods.

PyTorch implementation of Stein Variational Gradient Descent