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
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
A package for Multiple Kernel Learning in Python
ML4Chem: Machine Learning for Chemistry and Materials
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] Official Python implementation for Unsupervised Change Detection in Multitemporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
Multivariate Local Polynomial Regression and Radial Basis Function Regression
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"
Foundational library for Kernel methods in pattern analysis and machine learning
"GRAIL: Efficient Time-Series Representation Learning"
Toolkit for training quantum kernels in machine learning applications
This repository contains the TensorFlow implemtation of subgraph2vec (KDD MLG 2016) paper
ICML 2019. Turn a pre-trained GAN model into a content-addressable model without retraining.
Neural Tangent Kernel (NTK) module for the scikit-learn library
Learning with operator-valued kernels
Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.
Implementation of Generative Moment Matching Networks in pytorch
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