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1. |
Record Nr. |
UNINA9910710074403321 |
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Autore |
Crissman J. M |
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Titolo |
Reference standard polyethylene resins and piping materials : final report (October 1, 1986 - September 30, 1987) / / J. M. Crissman |
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Pubbl/distr/stampa |
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Gaithersburg, MD : , : U.S. Dept. of Commerce, National Institute of Standards and Technology, , 1988 |
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Descrizione fisica |
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Collana |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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1988. |
Contributed record: Metadata reviewed, not verified. Some fields updated by batch processes. |
Title from PDF title page. |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNISA996647970203316 |
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Autore |
Yu Han |
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Titolo |
Federated Learning in the Age of Foundation Models - FL 2024 International Workshops : FL@FM-WWW 2024, Singapore, May 14, 2024; FL@FM-ICME 2024, Niagara Falls, ON, Canada, July 15, 2024; FL@FM-IJCAI 2024, Jeju Island, South Korea, August 5, 2024; and FL@FM-NeurIPS 2024, Vancouver, BC, Canada, December 15, 2024, Revised Selected Papers / / edited by Han Yu, Xiaoxiao Li, Zenglin Xu, Randy Goebel, Irwin King |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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9783031822407 |
9783031822391 |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (295 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 15501 |
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Altri autori (Persone) |
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LiXiaoxiao |
XuZenglin |
GoebelRandy |
KingIrwin |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows: FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics: Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization. . |
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