A collection of important graph embedding, classification and representation learning papers with implementations.
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Updated
Mar 18, 2023 - Python
A collection of important graph embedding, classification and representation learning papers with implementations.
Learning kernels to maximize the power of MMD tests
Scala Library/REPL for Machine Learning Research
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
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.
ML4Chem: Machine Learning for Chemistry and Materials
A Matlab benchmarking toolbox for kernel adaptive filtering
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
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