Explainable Artificial Intelligence
portes grátis
Explainable Artificial Intelligence
Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II
Seifert, Christin; Lapuschkin, Sebastian; Longo, Luca
Springer International Publishing AG
07/2024
514
Mole
9783031637964
15 a 20 dias
Descrição não disponível.
.- XAI for graphs and Computer vision.
.- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
.- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
.- Explainable AI for Mixed Data Clustering.
.- Explaining graph classifiers by unsupervised node relevance attribution.
.- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
.- Graph Edits for Counterfactual Explanations: A comparative study.
.- Model guidance via explanations turns image classifiers into segmentation models.
.- Understanding the Dependence of Perception Model Competency on Regions in an Image.
.- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
.- Explainable Emotion Decoding for Human and Computer Vision.
.- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
.- Logic, reasoning, and rule-based explainable AI.
.- Template Decision Diagrams for Meta Control and Explainability.
.- A Logic of Weighted Reasons for Explainable Inference in AI.
.- On Explaining and Reasoning about Fiber Optical Link Problems.
.- Construction of artificial most representative trees by minimizing tree-based distance measures.
.- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
.- Model-agnostic and statistical methods for eXplainable AI.
.- Observation-specific explanations through scattered data approximation.
.- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
.- Local List-wise Explanations of LambdaMART.
.- Sparseness-Optimized Feature Importance.
.- Stabilizing Estimates of Shapley Values with Control Variates.
.- A Guide to Feature Importance Methods for Scientific Inference.
.- Interpretable Machine Learning for TabPFN.
.- Statistics and explainability: a fruitful alliance.
.- How Much Can Stratification Improve the Approximation of Shapley Values?.
.- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
.- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
.- Explainable AI for Mixed Data Clustering.
.- Explaining graph classifiers by unsupervised node relevance attribution.
.- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
.- Graph Edits for Counterfactual Explanations: A comparative study.
.- Model guidance via explanations turns image classifiers into segmentation models.
.- Understanding the Dependence of Perception Model Competency on Regions in an Image.
.- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
.- Explainable Emotion Decoding for Human and Computer Vision.
.- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
.- Logic, reasoning, and rule-based explainable AI.
.- Template Decision Diagrams for Meta Control and Explainability.
.- A Logic of Weighted Reasons for Explainable Inference in AI.
.- On Explaining and Reasoning about Fiber Optical Link Problems.
.- Construction of artificial most representative trees by minimizing tree-based distance measures.
.- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
.- Model-agnostic and statistical methods for eXplainable AI.
.- Observation-specific explanations through scattered data approximation.
.- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
.- Local List-wise Explanations of LambdaMART.
.- Sparseness-Optimized Feature Importance.
.- Stabilizing Estimates of Shapley Values with Control Variates.
.- A Guide to Feature Importance Methods for Scientific Inference.
.- Interpretable Machine Learning for TabPFN.
.- Statistics and explainability: a fruitful alliance.
.- How Much Can Stratification Improve the Approximation of Shapley Values?.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial intelligence;interpretable machine learning;causal inference & explanations;argumentative-based approaches for explanations;decomposition of neural network-based models for XAI;convolutional Neural Networks;natural language processing for explanations;reinforcement learning for enhancing XAI;explainability;neural networks;explainable Artificial Intelligence;Ante-hoc approaches for interpretability;Auto-encoders & explainability of latent spaces;Case-based explanations for AI systems;Graph neural networks for explainability;Interpreting & explaining Convolutional Neural Networks;Interpretable representational learning;Model-specific vs model-agnostic methods for XAI;Human rights for explanations in AI systems;Neuro-symbolic reasoning for XAI
.- XAI for graphs and Computer vision.
.- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
.- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
.- Explainable AI for Mixed Data Clustering.
.- Explaining graph classifiers by unsupervised node relevance attribution.
.- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
.- Graph Edits for Counterfactual Explanations: A comparative study.
.- Model guidance via explanations turns image classifiers into segmentation models.
.- Understanding the Dependence of Perception Model Competency on Regions in an Image.
.- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
.- Explainable Emotion Decoding for Human and Computer Vision.
.- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
.- Logic, reasoning, and rule-based explainable AI.
.- Template Decision Diagrams for Meta Control and Explainability.
.- A Logic of Weighted Reasons for Explainable Inference in AI.
.- On Explaining and Reasoning about Fiber Optical Link Problems.
.- Construction of artificial most representative trees by minimizing tree-based distance measures.
.- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
.- Model-agnostic and statistical methods for eXplainable AI.
.- Observation-specific explanations through scattered data approximation.
.- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
.- Local List-wise Explanations of LambdaMART.
.- Sparseness-Optimized Feature Importance.
.- Stabilizing Estimates of Shapley Values with Control Variates.
.- A Guide to Feature Importance Methods for Scientific Inference.
.- Interpretable Machine Learning for TabPFN.
.- Statistics and explainability: a fruitful alliance.
.- How Much Can Stratification Improve the Approximation of Shapley Values?.
.- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems.
.- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.
.- Explainable AI for Mixed Data Clustering.
.- Explaining graph classifiers by unsupervised node relevance attribution.
.- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.
.- Graph Edits for Counterfactual Explanations: A comparative study.
.- Model guidance via explanations turns image classifiers into segmentation models.
.- Understanding the Dependence of Perception Model Competency on Regions in an Image.
.- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.
.- Explainable Emotion Decoding for Human and Computer Vision.
.- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.
.- Logic, reasoning, and rule-based explainable AI.
.- Template Decision Diagrams for Meta Control and Explainability.
.- A Logic of Weighted Reasons for Explainable Inference in AI.
.- On Explaining and Reasoning about Fiber Optical Link Problems.
.- Construction of artificial most representative trees by minimizing tree-based distance measures.
.- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.
.- Model-agnostic and statistical methods for eXplainable AI.
.- Observation-specific explanations through scattered data approximation.
.- CNN-based explanation ensembling for dataset, representation and explanations evaluation.
.- Local List-wise Explanations of LambdaMART.
.- Sparseness-Optimized Feature Importance.
.- Stabilizing Estimates of Shapley Values with Control Variates.
.- A Guide to Feature Importance Methods for Scientific Inference.
.- Interpretable Machine Learning for TabPFN.
.- Statistics and explainability: a fruitful alliance.
.- How Much Can Stratification Improve the Approximation of Shapley Values?.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial intelligence;interpretable machine learning;causal inference & explanations;argumentative-based approaches for explanations;decomposition of neural network-based models for XAI;convolutional Neural Networks;natural language processing for explanations;reinforcement learning for enhancing XAI;explainability;neural networks;explainable Artificial Intelligence;Ante-hoc approaches for interpretability;Auto-encoders & explainability of latent spaces;Case-based explanations for AI systems;Graph neural networks for explainability;Interpreting & explaining Convolutional Neural Networks;Interpretable representational learning;Model-specific vs model-agnostic methods for XAI;Human rights for explanations in AI systems;Neuro-symbolic reasoning for XAI
