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pro vyhledávání: '"Siems, A."'
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Autor:
Bruder, Harold
Publikováno v:
Opera Quarterly. Summer99, Vol. 15 Issue 3, p390. 11p. 3 Black and White Photographs.
Autor:
Heidrun Wiesenmüller, Achim Oßwald
Publikováno v:
o-bib. Das offene Bibliotheksjournal, Vol 9, Iss 3 (2022)
Debatte um den Artikel „Das Lesen der Anderen. Die Auswirkungen von User Tracking auf Bibliotheken“ von Renke Siems. Reaktion der geschäftsführenden Herausgebenden auf eine Aufforderung zur „Richtigstellung“ von OCLC. Das Schreiben von OCLC
Externí odkaz:
https://doaj.org/article/4062f4b3b2654cbb95f991fb87a223e3
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba4Cast generalizes robustly across diverse time series tasks without th
Externí odkaz:
http://arxiv.org/abs/2410.09385
Autor:
Mueller, Andreas, Siems, Julien, Nori, Harsha, Salinas, David, Zela, Arber, Caruana, Rich, Hutter, Frank
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such as splines, boosted trees, or
Externí odkaz:
http://arxiv.org/abs/2410.04560
Autor:
Marique, Yseult
Publikováno v:
Edinburgh Law Review. 2016, Vol. 20 Issue 1, p109-111. 3p.
We propose a scalable framework for deciding, proving, and explaining (in)equivalence of context-free grammars. We present an implementation of the framework and evaluate it on large data sets collected within educational support systems. Even though
Externí odkaz:
http://arxiv.org/abs/2407.18220
Autor:
Kindler, Peter
Publikováno v:
Rabels Zeitschrift für ausländisches und internationales Privatrecht / The Rabel Journal of Comparative and International Private Law, 2020 Oct 01. 84(4), 901-905.
Externí odkaz:
https://www.jstor.org/stable/45384152
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information
Externí odkaz:
http://arxiv.org/abs/2402.03170
Publikováno v:
Asef, P, Taheri, R, Shojafar, M, Mporas, I & Tafazolli, R 2023, ' SIEMS : A Secure Intelligent Energy Management System for Industrial IoT applications ', IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 1039-1050 . https://doi.org/10.1109/TII.2022.3165890
In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f7d870ffaf85ef7f1ace0da27834682
https://purehost.bath.ac.uk/ws/files/246666037/SIEMS_Proofread.pdf
https://purehost.bath.ac.uk/ws/files/246666037/SIEMS_Proofread.pdf