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
Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
[IEEE TCYB 2021] Official Python implementation for 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
Kernel Methods Toolbox for Matlab/Octave
Implementation of LMS, RLS, KLMS and KRLS filters in Python
This is the page for the book Digital Signal Processing with Kernel Methods.
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