A collection of important graph embedding, classification and representation learning papers with implementations.
-
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
A Matlab benchmarking toolbox for kernel adaptive filtering
A package for Multiple Kernel Learning in Python
"GRAIL: Efficient Time-Series Representation Learning"
SPLASH is an interactive visualisation and plotting tool using kernel interpolation, mainly used for Smoothed Particle Hydrodynamics simulations
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Kernel Methods Toolbox for Matlab/Octave
NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.
Large-scale, multi-GPU capable, kernel solver
A python package for graph kernels, graph edit distances, and graph pre-image problem.
Fast radial basis function interpolation for large scale data
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"
[IEEE TCYB 2021] Official Python implementation for Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
This is the page for the book Digital Signal Processing with Kernel Methods.
NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
ML4Chem: Machine Learning for Chemistry and Materials
Undetected Call of duty: MW, Warzone kernel injector.
This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
Add a description, image, and links to the kernel-methods topic page so that developers can more easily learn about it.
To associate your repository with the kernel-methods topic, visit your repo's landing page and select "manage topics."