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pro vyhledávání: '"Hess, Florian"'
In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any scientific discipline, where we are particularly interested in parsimoni
Externí odkaz:
http://arxiv.org/abs/2406.04934
The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI and OpenAI. This situation of legal uncertainty has sparked a broad disc
Externí odkaz:
http://arxiv.org/abs/2404.02309
In science we are interested in finding the governing equations, the dynamical rules, underlying empirical phenomena. While traditionally scientific models are derived through cycles of human insight and experimentation, recently deep learning (DL) t
Externí odkaz:
http://arxiv.org/abs/2402.18377
Publikováno v:
PMLR 202:13017-13049, 2023
Chaotic dynamical systems (DS) are ubiquitous in nature and society. Often we are interested in reconstructing such systems from observed time series for prediction or mechanistic insight, where by reconstruction we mean learning geometrical and inva
Externí odkaz:
http://arxiv.org/abs/2306.04406
Many, if not most, systems of interest in science are naturally described as nonlinear dynamical systems. Empirically, we commonly access these systems through time series measurements. Often such time series may consist of discrete random variables
Externí odkaz:
http://arxiv.org/abs/2212.07892
Autor:
Hess, Florian, Tomczak, Leonard
$A$ be an abelian variety over a number field $K$ of dimension $r$, $a_1, \dots, a_g \in A(K)$ and $F/K$ a finite Galois extension. We consider the density of primes $\frak p$ of $K$ such that the quotient $\bar{A}(k({\frak p}))/\langle \bar{a}_1,\do
Externí odkaz:
http://arxiv.org/abs/2212.03386
Autor:
Brenner, Manuel, Hess, Florian, Mikhaeil, Jonas M., Bereska, Leonard, Monfared, Zahra, Kuo, Po-Chen, Durstewitz, Daniel
In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise. Previous deep learning approaches toward this goal o
Externí odkaz:
http://arxiv.org/abs/2207.02542
Autor:
Hess, Florian1,2 (AUTHOR), Kipping, Thomas1 (AUTHOR), Weitschies, Werner2 (AUTHOR), Krause, Julius2 (AUTHOR) julius.krause@uni-greifswald.de
Publikováno v:
Pharmaceutics. Apr2024, Vol. 16 Issue 4, p472. 21p.
Autor:
Gottschalk, Nadine, Quodbach, Julian, Elia, Alessandro-Giuseppe, Hess, Florian, Bogdahn, Malte
Publikováno v:
In International Journal of Pharmaceutics 25 February 2022 614
Publikováno v:
In Journal of Shoulder and Elbow Surgery February 2020 29(2):308-315