Overview of “Graph Neural Networks: Foundations, Frontiers, and Applications”
Introduction
We will provide an overview of "Graph Neural Networks: Foundations, Frontiers, and Applicatio...
説明できる機械学習
Overview of Explainability in GNNs and Examples of Algorithms and Implementations
Overview of Explainability in GNN
GNNs (Graph Neural Networks) are neural networks for handling graph-stru...
Overview of Message Passing in Machine Learning with Algorithms and Examples of Implementations
Message Passing in Machine Learning
Message passing in machine learning is an effective approach to data a...
Various methods of machine learning that can be explained and examples of implementations
Explainable Machine Learning
Explainable Machine Learning (EML) refers to methods and approaches that explai...
Protected: Explainable Machine Learning (19)prototype and criticism
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Protected: Explainable Machine Learning (17) Counterfactual Explanations
Explanation of machine learning results by counterfactual explanations utilized in digital transformation, artificial intelligence, and machine learning tasks Anchor, Growing Spheres algorithm, Python, Alibi, categorical features, Rashomon effect, LIME, fully coupled neural networks, counterfactual generation algorithms, Euclidean distance, central absolute deviation, Nelder-Mead method, causal semantics, causes
Protected: Explainable Artificial Intelligence (16) Model independent interpretation (SHAP (SHapley Additive exPlanations))
Model independent interpretation with SHAP as an explainable artificial intelligence used for digital transformation, artificial intelligence and machine learning tasks scikit-learn, xgboost, LightGBM, tree boosting, R, shapper, fastshap, TreeSHAP, KernelSHAP, partial dependence plot, permutation feature importance, feature importance, feature dependence, interactions, clustering, summary plots clustering, summary plots, atomic unit, LIME, decision tree, game theory, clustering, SHAP interaction values, ALE plot, image mapping, consistency, missing, local correctness, efficiency, symmetry, dummyness, additivity, SHapley Additive exPlanations, local surrogate models
Protected: Explainable Artificial Intelligence (14)Model Independent Interpretation (Scoped Rules (Anchors))
Model-independent interpretation with Anchor as explainable machine learning leveraged for digital transformation, artificial intelligence, and machine learningtasks Python, anchor, Alibi, Java, Anchors, BatchSAR, tabular data, Multi-Armed Bandit, KL-LUCB, reinforcement learning, graph search algorithms, LIME