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pro vyhledávání: '"Nickl, Peter"'
Autor:
Shen, Yuesong, Daheim, Nico, Cong, Bai, Nickl, Peter, Marconi, Gian Maria, Bazan, Clement, Yokota, Rio, Gurevych, Iryna, Cremers, Daniel, Khan, Mohammad Emtiyaz, Möllenhoff, Thomas
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for
Externí odkaz:
http://arxiv.org/abs/2402.17641
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to pert
Externí odkaz:
http://arxiv.org/abs/2310.19273
Well-calibrated probabilistic regression models are a crucial learning component in robotics applications as datasets grow rapidly and tasks become more complex. Unfortunately, classical regression models are usually either probabilistic kernel machi
Externí odkaz:
http://arxiv.org/abs/2211.01120
Probabilistic regression techniques in control and robotics applications have to fulfill different criteria of data-driven adaptability, computational efficiency, scalability to high dimensions, and the capacity to deal with different modalities in t
Externí odkaz:
http://arxiv.org/abs/2011.05217
Akademický článek
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Philosophie Als „Scientia Affectiva“?: Ein mittelalterlicher Begriff und seine Spuren in der Neuzeit
Autor:
Nickl, Peter, author
Publikováno v:
Perspektiven der Philosophie: Neues Jahrbuch. Band 31 – 2005. 31:47-70
Autor:
Nickl, Peter
Diss.--Fakultät für Philosophie, Wissenschaftstheorie und Statistik--München--Ludwig-Maximilians-Universität, 1990-1991.
Externí odkaz:
http://catalogue.bnf.fr/ark:/12148/cb35587587j
Well-calibrated probabilistic regression models are a crucial learning component in robotics applications as datasets grow rapidly and tasks become more complex. Classical regression models are usually either probabilistic kernel machines with a flex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff1aeb17607a3ebd4827243ae294d6a3
http://arxiv.org/abs/2211.01120
http://arxiv.org/abs/2211.01120