Explainable Machine Learning (Thessaloniki Machine Learning Meetup)
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Updated
May 20, 2019 - Jupyter Notebook
Explainable Machine Learning (Thessaloniki Machine Learning Meetup)
Code for paper https://arxiv.org/abs/1910.04256
📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. Thai Le, Suhang Wang, Dongwon Lee. 26th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining (KDD)
A curated list of papers on explainability and interpretability of self-driving models
A simple and explainable deep learning model for NLP.
This module extends the kernel SHAP method (as introduced by Lundberg and Lee (2017)) which is local in nature, to a method that computes global SHAP values.
A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
CT scan machine learning models including AxialNet and HiResCAM
explainable
Trustworthy LoS Prediction Based on Multi-modal Data (AIME 2023)
'Explainable' deep learning anomaly detection methods compatible with dynamic graph data
📍 Interactive Studio for Explanatory Model Analysis
Final report and implementation of my systems to help groups make decisions using arguments
SurvSHAP(t): Time-dependent explanations of machine learning survival models
Python framework for explainable omics analysis
A Explainable Artificial Intelligence tool focused in ensemble black box models, based in Item Response Theory, called eXirt.
A 🐶🐱 explanation of generative neural nets
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