Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Reyes, Kristofer G."'
Asynchronous Bayesian optimization is a recently implemented technique that allows for parallel operation of experimental systems and disjointed workflows. Contrasting with serial Bayesian optimization which individually selects experiments one at a
Externí odkaz:
http://arxiv.org/abs/2406.15291
The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is driven by numerous factors. An uncertainty-aware metric that can quant
Externí odkaz:
http://arxiv.org/abs/2301.05763
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to the GP's analytical tractability, robustness, non-parametric structure,
Externí odkaz:
http://arxiv.org/abs/2205.09070
Multi-stage screening pipelines are ubiquitous throughout experimental and computational science. Much of the effort in developing screening pipelines focuses on improving generative methods or surrogate models in an attempt to make each screening st
Externí odkaz:
http://arxiv.org/abs/2203.01143
Autor:
Baek, Soojung, Reyes, Kristofer G.
We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many
Externí odkaz:
http://arxiv.org/abs/2103.07776
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Gongora, Aldair E., Snapp, Kelsey L., Whiting, Emily, Riley, Patrick, Reyes, Kristofer G., Morgan, Elise F., Brown, Keith A.
Publikováno v:
In iScience 23 April 2021 24(4)
Autor:
Li, Yan, Reyes, Kristofer G., Vazquez-Anderson, Jorge, Wang, Yingfei, Contreras, Lydia M., Powell, Warren B.
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be in
Externí odkaz:
http://arxiv.org/abs/1508.01551
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Sadeghi, Sina, Bateni, Fazel, Kim, Taekhoon, Son, Dae Yong, Bennett, Jeffrey A., Orouji, Negin, Punati, Venkat S., Stark, Christine, Cerra, Teagan D., Awad, Rami, Delgado-Licona, Fernando, Xu, Jinge, Mukhin, Nikolai, Dickerson, Hannah, Reyes, Kristofer G., Abolhasani, Milad
Publikováno v:
Nanoscale; 1/14/2024, Vol. 16 Issue 2, p580-591, 12p