A pytorch implementation of MINE(Mutual Information Neural Estimation)

JIDT: Java Information Dynamics Toolkit for studying information-theoretic measures of computation in complex systems

Code for CIKM2020 "S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization"

MINE: Mutual Information Neural Estimation in pytorch (unofficial)

PyTorch implementation for Interpretable Dialog Generation ACL 2018, It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

Mutual Information functions for C and MATLAB

Python3 binding to mRMR Feature Selection algorithm (currently not maintained)

Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python

Algorithms for detecting associations, dynamical influences and causal inference from data.

新词发现 基于词频、凝聚系数和左右邻接信息熵

(NeurIPS 2020) Transductive Information Maximization for Few-Shot Learning https://arxiv.org/abs/2008.11297

auto-calibration of lidar and camera based on maximization of intensity mutual information

Pytorch implementation of Learning Disentangled Representations via Mutual Information Estimation (ECCV 2020)

Mutual Information Tools for protein Sequence analysis in Julia

Mutual Information Neural Estimator implemented in Tensorflow

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Code for GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation (SIGIR 2020)

DRMIME - Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration

R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test

This is our standard library for nonlinear analysis. Many of these functions are the same we use in our services. We do have additional methods that are not public but could be made available in a future release. If you are interested in learning more, attending our workshops or webinars or using our data analysis services please contact bmchnon…