BetaML
BetaML: The Beta Machine Learning Toolkit, a self-contained repository of Machine Learning algorithms in Julia - Published in JOSS (2021)
hierarchical-dnn-interpretations
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
https://github.com/csinva/disentangled-attribution-curves
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
https://github.com/cedergrouphub/s4
Solid-state synthesis science analyzer. Thermo, features, ML, and more.
https://github.com/csinva/transformation-importance
Using / reproducing TRIM from the paper "Transformation Importance with Applications to Cosmology" 🌌 (ICLR Workshop 2020)
prophitbet-soccer-bets-predictor
ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.
https://github.com/cn-tu/adversarial-recurrent-ids
Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").
verbal-explanations-of-spatio-temporal-graph-neural-networks-for-traffic-forecasting
An eXplainable AI system to elucidate short-term speed forecasts in traffic networks obtained by Spatio-Temporal Graph Neural Networks.