Results 31 to 40 of about 288,690 (232)
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Bayesian Optimization with Expensive Integrands
SIAM Journal on Optimization, 2022 We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential design of experiments with random ...
Saul Toscano-Palmerin, Peter I. Frazier
openaire +3 more sources
Efficacy of Inebilizumab in N‐MOmentum Trial Participants With or Without Prior Immunosuppressants
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT This post hoc analysis examined the impact of prior immunosuppressants on the long‐term efficacy and safety of inebilizumab, a cluster of differentiation 19+ B‐cell–depleting monoclonal antibody, in participants with aquaporin‐4–seropositive neuromyelitis optica spectrum disorder from the N‐MOmentum trial (NTC02200770).
Bruce A. C. Cree +9 more
wiley +1 more source
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits [PDF]
, 2014 BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems.
Martinez-Cantin, Ruben
core +1 more source
, 2019 Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication ...
Burger, Sven +5 more
core +1 more source
Bayesian Optimization in AlphaGo
CoRR, 2018 During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvements in playing strength. For example, prior to the match with Lee Sedol, we tuned the latest AlphaGo agent and this improved its win-rate from 50% to 66.5% in self-play games ...
Yutian Chen 0001 +6 more
openaire +2 more sources
A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi +17 more
wiley +1 more source
Bayesian Optimization with Gradients
CoRR, 2017 Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective ...
Jian Wu +3 more
openaire +3 more sources
Functional Causal Bayesian Optimization
CoRR, 2023 We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family of methods to enable functional interventions, which set a variable to be a deterministic function of other variables in the graph.
Limor Gultchin +3 more
openaire +3 more sources