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aane_matlab

Accelerated Attributed Network Embedding, SDM 2017

bayesian-coresets

Automated Scalable Bayesian Inference

conjugategradientclassifier

Learning Parameters for training data for linear and logistic regression via conjugate gradient optimization

cxhsmm

Source codes for the Coxian Hidden Semi-Markov Models (CxHSMM)

fgm

flow-gan

Code for "Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models", AAAI 2018.

gaimc

Graph Algorithms in Matlab Code

generalized-lloyd-quantization

An implementation of the Generalized Lloyd method for vector quantization

glow

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

graphhopper

graphhopper-kernels

Scalable kernels for graphs with continuous attributes (Feragen et al., NIPS 2013) http://goo.gl/VxSfzZ

graphkernels

hashgraphkernel

Source code for our IEEE ICDM 2016 paper "Faster Kernels for Graphs with Continuous Attributes".

hctsa

Highly comparative time-series analysis code repository

hsca

Code for ICDM-2016 paper "Homophily, Structure, and Content Augmented Network Representation Learning"

imatlab

A Jupyter kernel for MATLAB.

integer_discrete_flows

Code release for Hoogeboom, Emiel, Jorn WT Peters, Rianne van den Berg, and Max Welling. "Integer Discrete Flows and Lossless Compression." Conference on Neural Information Processing Systems (2019).

invertible-resnet

Official Code for Invertible Residual Networks

libsvm

lomap

Alchemical mutation scoring map

matlab-hsmm

matlab-network-utilities

A collection of utility programs in MATLAB for use on Complex Networks

matlab_kernel

Jupyter Kernel for Matlab

mcs

Maximum Common Substructure

mcstracker

node2vec

open-mcs

An open implementation of the MC family of maximum clique algorithms

spdz-2

(Inactive, see readme) Multiparty computation with SPDZ, MASCOT, and Overdrive offline phases

tensorflow

An Open Source Machine Learning Framework for Everyone