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Produktbild: Explainable Artificial Intelligence
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Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I

Aus der Reihe Communications in Computer and Information Science
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76,99 € UVP 87,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 494 p. 143 illus., 137 illus. in color.

Herausgeber

Luca Longo + weitere

Verlag

Springer

Seitenzahl

494

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

768 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63786-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 494 p. 143 illus., 137 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

494

Maße (L/B/H)

23,5/15,5/2,8 cm

Gewicht

768 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63786-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Explainable Artificial Intelligence

  • .- Intrinsically interpretable XAI and concept-based global explainability.

    .- Seeking Interpretability and Explainability in Binary Activated Neural Networks.

    .- Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges.

    .- Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model.

    .- Revisiting FunnyBirds evaluation framework for prototypical parts networks.

    .- CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models.

    .- Unveiling the Anatomy of Adversarial Attacks: Concept-based XAI Dissection of CNNs.

    .- AutoCL: AutoML for Concept Learning.

    .- Locally Testing Model Detections for Semantic Global Concepts.

    .- Knowledge graphs for empirical concept retrieval.

    .- Global Concept Explanations for Graphs by Contrastive Learning.

    .- Generative explainable AI and verifiability.

    .- Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation.

    .- Generative Inpainting for Shapley-Value-Based Anomaly Explanation.

    .- Challenges and Opportunities in Text Generation Explainability.

    .- NoNE Found: Explaining the Output of Sequence-to-Sequence Models when No Named Entity is Recognized.

    .- Notion, metrics, evaluation and benchmarking for XAI.

    .- Benchmarking Trust: A Metric for Trustworthy Machine Learning.

    .- Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI.

    .- Conditional Calibrated Explanations: Finding a Path between Bias and Uncertainty.

    .- Meta-evaluating stability measures: MAX-Sensitivity & AVG-Senstivity.

    .- Xpression: A unifying metric to evaluate Explainability and Compression of AI models.

    .- Evaluating Neighbor Explainability for Graph Neural Networks.

    .- A Fresh Look at Sanity Checks for Saliency Maps.

    .- Explainability, Quantified: Benchmarking XAI techniques.

    .- BEExAI: Benchmark to Evaluate Explainable AI.

    .- Associative Interpretability of Hidden Semantics with Contrastiveness Operators in Face Classification tasks.