kernel-methods

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

Scala Library/REPL for Machine Learning Research

Learning kernels to maximize the power of MMD tests

Official pytorch implementation of the paper "Deep Kernel Transfer in Gaussian Processes for Few-shot Learning"

Large-scale, multi-GPU capable, kernel solver

A package for Multiple Kernel Learning in Python

ML4Chem: Machine Learning for Chemistry and Materials

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

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

A Matlab benchmarking toolbox for kernel adaptive filtering

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

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

Contains the code (and working vm setup) for our KDD MLG 2016 paper titled: "subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs"

FRP: Fast Random Projections

Kernel Methods Toolbox for Matlab/Octave

Foundational library for Kernel methods in pattern analysis and machine learning

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

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

This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.

This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper

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

PyTorch implementation of Stein Variational Gradient Descent

ICML 2019. Turn a pre-trained GAN model into a content-addressable model without retraining.

Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.

NeurIPS 2018. Linear-time model comparison tests.

Implicit generative models and related stuff based on the MMD, in PyTorch

Curated materials for different machine learning related summer schools

Learning with operator-valued kernels

UAI 2015. Kernel-based just-in-time learning for expectation propagation