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Machine Learning and Knowledge Discovery in Databases [electronic resource] : European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III / edited by Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas.

Contributor(s): Amini, Massih-Reza [editor.] | Canu, Stéphane [editor.] | Fischer, Asja [editor.] | Guns, Tias [editor.] | Kralj Novak, Petra [editor.] | Tsoumakas, Grigorios [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Artificial Intelligence: 13715Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XLVI, 683 p. 204 illus., 194 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031264092.Subject(s): Artificial intelligence | Computer science -- Mathematics | Computer vision | Computer networks  | Computers, Special purpose | Computer engineering | Artificial Intelligence | Mathematics of Computing | Computer Vision | Computer Communication Networks | Special Purpose and Application-Based Systems | Computer Engineering and NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Deep learning -- robust and adversarial machine learning -- generative models -- computer vision -- meta-learning, neural architecture search.
In: Springer Nature eBookSummary: The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
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Deep learning -- robust and adversarial machine learning -- generative models -- computer vision -- meta-learning, neural architecture search.

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

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