Zobrazeno 1 - 10
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pro vyhledávání: '"Gerstenberg"'
Autor:
Hjort Møller, Andreas1 (AUTHOR) andreashjortmoller@gmail.com
Publikováno v:
European Journal of Scandinavian Studies. Oct2022, Vol. 52 Issue 2, p185-202. 18p.
Autor:
Paulsen, Adam1 (AUTHOR) adpa@sdu.dk
Publikováno v:
European Journal of Scandinavian Studies. Oct2022, Vol. 52 Issue 2, p203-222. 20p.
Autor:
Karl Langosch, Burghart Wachinger, Gundolf Keil, Kurt Ruh, Werner Schröder, Franz Josef Worstbrock, Christine Stöllinger-Löser
„DAS heuristische Spitzenwerk der Mittelaltergermanistik, das für alle mediävistischen Disziplinen bis hin zur Medizingeschichte unentbehrlich ist. Das Werk dokumentiert nicht nur weitgehend umfassend den gegenwärtigen Forschungsstand innerhalb
Akademický článek
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Autor:
Jin, Emily, Huang, Zhuoyi, Fränken, Jan-Philipp, Liu, Weiyu, Cha, Hannah, Brockbank, Erik, Wu, Sarah, Zhang, Ruohan, Wu, Jiajun, Gerstenberg, Tobias
Reconstructing past events requires reasoning across long time horizons. To figure out what happened, we need to use our prior knowledge about the world and human behavior and draw inferences from various sources of evidence including visual, languag
Externí odkaz:
http://arxiv.org/abs/2410.01926
Autor:
Gandhi, Kanishk, Lynch, Zoe, Fränken, Jan-Philipp, Patterson, Kayla, Wambu, Sharon, Gerstenberg, Tobias, Ong, Desmond C., Goodman, Noah D.
Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a variety of other affective cognition. How adept is modern AI at these in
Externí odkaz:
http://arxiv.org/abs/2409.11733
We introduce a novel class of algorithms to efficiently approximate the unknown return distributions in policy evaluation problems from distributional reinforcement learning (DRL). The proposed distributional dynamic programming algorithms are suitab
Externí odkaz:
http://arxiv.org/abs/2407.14175
When faced with a novel scenario, it can be hard to succeed on the first attempt. In these challenging situations, it is important to know how to retry quickly and meaningfully. Retrying behavior can emerge naturally in robots trained on diverse data
Externí odkaz:
http://arxiv.org/abs/2406.15917