Zobrazeno 1 - 10
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pro vyhledávání: '"Kupper, A"'
Evaluating modern ML models is hard. The strong incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad practices which fall short of outright research frau
Externí odkaz:
http://arxiv.org/abs/2407.12220
The string topology coproduct is often perceived as a counterpart in string topology to the Chas-Sullivan product. However, in certain aspects the string topology coproduct is much harder to understand than the Chas-Sullivan product. In particular th
Externí odkaz:
http://arxiv.org/abs/2404.03460
We introduce the concept evolutionary semigroups on path spaces, generalizing the notion of transition semigroups to possibly non-Markovian stochastic processes. We study the basic properties of evolutionary semigroups and, in particular, prove that
Externí odkaz:
http://arxiv.org/abs/2403.13386
Autor:
Kupper, M., Zapata, J. M.
In decision-making, maxitive functions are used for worst-case and best-case evaluations. Maxitivity gives rise to a rich structure that is well-studied in the context of the pointwise order. In this article, we investigate maxitivity with respect to
Externí odkaz:
http://arxiv.org/abs/2403.06613
Autor:
Felfeliyan, Banafshe, Zhou, Yuyue, Ghosh, Shrimanti, Kupper, Jessica, Liu, Shaobo, Hareendranathan, Abhilash, Jaremko, Jacob L.
Osteoarthritis (OA) poses a global health challenge, demanding precise diagnostic methods. Current radiographic assessments are time consuming and prone to variability, prompting the need for automated solutions. The existing deep learning models for
Externí odkaz:
http://arxiv.org/abs/2401.06331
In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss co
Externí odkaz:
http://arxiv.org/abs/2312.05973
We provide explicit convergence rates for Chernoff-type approximations of convex monotone semigroups which have the form $S(t)f=\lim_{n\to\infty}I(\frac{t}{n})^n f$ for bounded continuous functions $f$. Under suitable conditions on the one-step opera
Externí odkaz:
http://arxiv.org/abs/2310.09830
We present a novel, fast (exponential rate adaption), ab initio (hyper-parameter-free) gradient based optimizer algorithm. The main idea of the method is to adapt the learning rate $\alpha$ by situational awareness, mainly striving for orthogonal nei
Externí odkaz:
http://arxiv.org/abs/2309.06274
Publikováno v:
Atmospheric Measurement Techniques, Vol 17, Pp 4649-4658 (2024)
Emissions from agricultural sources substantially contribute to global warming. The inverse dispersion method (IDM) has been successfully used for emission measurements from various agricultural sources. The IDM has also been validated in multiple st
Externí odkaz:
https://doaj.org/article/b33b72a2f3a34839a29ae96201bc77a9
Based on the convergence of their infinitesimal generators in the mixed topology, we provide a stability result for strongly continuous convex monotone semigroups on spaces of continuous functions. In contrast to previous results, we do not rely on t
Externí odkaz:
http://arxiv.org/abs/2305.18981