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pro vyhledávání: '"Jason, M."'
Autor:
Kilcrease, Jack D.
Publikováno v:
Anglican and Episcopal History, 2024 Mar 01. 93(1), 250-251.
Externí odkaz:
https://www.jstor.org/stable/27290786
Autor:
Peterson, Josiah
Publikováno v:
Mythlore, 2023 Apr 01. 412(142), 229-235.
Externí odkaz:
https://www.jstor.org/stable/48722624
Autor:
Damian Hubiak
Publikováno v:
Studia Antiquitatis et Medii Aevi Incohantis, Vol 7 (2022), Pp 108-110 (2022)
Externí odkaz:
https://doaj.org/article/4aaa429329834632ba3c21502914747f
Akademický článek
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Autor:
Pittman, Jason M.
Machine learning systems increasingly drive innovation across scientific fields and industry, yet challenges in compute overhead, specifically during inference, limit their scalability and sustainability. Responsible AI guardrails, essential for ensu
Externí odkaz:
http://arxiv.org/abs/2412.19241
Autor:
Altschuler, Jason M., Chewi, Sinho
Coupling arguments are a central tool for bounding the deviation between two stochastic processes, but traditionally have been limited to Wasserstein metrics. In this paper, we apply the shifted composition rule--an information-theoretic principle in
Externí odkaz:
http://arxiv.org/abs/2412.17997
Autor:
Brown, Nina M., VanSaders, Bryan, Kronenfeld, Jason M., DeSimone, Joseph M., Jaeger, Heinrich M.
Cohesive granular materials are found in many natural and industrial environments, but experimental platforms for exploring the innate mechanical properties of these materials are often limited by the difficulty of adjusting cohesion strength. Granul
Externí odkaz:
http://arxiv.org/abs/2412.13282
An organism that is newly introduced into an existing population has a survival probability that is dependent on both the population density of its environment and the competition it experiences with the members of that population. Expanding populati
Externí odkaz:
http://arxiv.org/abs/2412.10937
We show that for separable convex optimization, random stepsizes fully accelerate Gradient Descent. Specifically, using inverse stepsizes i.i.d. from the Arcsine distribution improves the iteration complexity from $O(k)$ to $O(k^{1/2})$, where $k$ is
Externí odkaz:
http://arxiv.org/abs/2412.05790
Autor:
Bok, Jinho, Altschuler, Jason M.
Surprisingly, recent work has shown that gradient descent can be accelerated without using momentum -- just by judiciously choosing stepsizes. An open question raised by several papers is whether this phenomenon of stepsize-based acceleration holds m
Externí odkaz:
http://arxiv.org/abs/2412.05497