fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf
fachmedien.de - spezialisierte Fachinformationen für Studium und Beruf

Site Breadcrumb

Verkauf durch Sack Fachmedien

Longo / Seifert / Lapuschkin

Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part II

Medium: Buch
ISBN: 978-3-031-63796-4
Verlag: Springer Nature Switzerland
Erscheinungstermin: 10.07.2024
In den Warenkorb
Lieferfrist: bis zu 10 Tage
Auf die Merkliste Drucken
Leseprobe
Überschrift

Beschreibung

Kostenloser Versand

nach Deutschland, Österreich und in die Schweiz

Bequem zahlen

Rechnung, Kreditkarte, PayPal oder Bankeinzug

Wir sind gerne für Sie da

0800 000-1378 (Inland)
service@fachmedien.de
<<Kontaktformular>>

Fragen

Fragen Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua
Anwtorten

Anwtorten At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet

This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024. 

The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on:

Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI.

Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI.

Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI.

Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.

Produkteigenschaften

  • Artikelnummer: 9783031637964
  • Medium: Buch
  • ISBN: 978-3-031-63796-4
  • Verlag: Springer Nature Switzerland
  • Erscheinungstermin: 10.07.2024
  • Sprache(n): Englisch
  • Auflage: 2024
  • Serie: Communications in Computer and Information Science
  • Produktform: Kartoniert
  • Gewicht: 797 g
  • Seiten: 514
  • Format (B x H x T): 155 x 235 x 29 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

.- 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?.