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pro vyhledávání: '"Schön, Thomas"'
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression,
Externí odkaz:
http://arxiv.org/abs/1909.12297
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feasible for problems where the data consists of line integral measurement
Externí odkaz:
http://arxiv.org/abs/1909.01844
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convoluti
Externí odkaz:
http://arxiv.org/abs/1909.01730
Autor:
Wills, Adrian, Schön, Thomas
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems because they afford rapid convergence and computationally attractive a
Externí odkaz:
http://arxiv.org/abs/1909.01238
Autor:
Chen, Ricky Hao, Michael, Toni, Kuhlin, Johanna, Schön, Thomas, Stocker, Sophie, Alffenaar, Jan-Willem C.
Publikováno v:
In International Journal of Antimicrobial Agents September 2023 62(3)
Akademický článek
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Akademický článek
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Autor:
Fröberg, Gabrielle, Maurer, Florian P., Chryssanthou, Erja, Fernström, Louise, Benmansour, Hanaa, Boarbi, Samira, Mengshoel, Anne Torunn, Keller, Peter Michael, Viveiros, Miguel, Machado, Diana, Fitzgibbon, Margaret M., Mok, Simone, Werngren, Jim, Cirillo, Daniela Maria, Alcaide, Fernando, Hyyryläinen, Hanne-Leena, Aubry, Alexandra, Andres, Sönke, Nadarajan, Darshaalini, Svensson, Erik, Turnidge, John, Giske, Christian G., Kahlmeter, Gunnar, Cambau, Emmanuelle, van Ingen, Jakko, Schön, Thomas
Publikováno v:
In Clinical Microbiology and Infection June 2023 29(6):758-764
Publikováno v:
Conference on Uncertainty in Artificial Intelligence (UAI) 2019
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwellization via delayed sampling. B
Externí odkaz:
http://arxiv.org/abs/1907.04615
The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade. In this paper, we argue that this principle, while pow
Externí odkaz:
http://arxiv.org/abs/1906.08482