Results 61 to 70 of about 9,437,008 (290)
Loss-Driven Bayesian Active Learning
CoRRThe central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acquisition to different downstream problems and losses. We propose a rigorous loss-driven approach to Bayesian active learning that allows data acquisition to directly target ...
Zhuoyue, H +2 more
openaire +4 more sources
Accelerating Dental Adhesive Innovations Through Active Learning and Bayesian Optimization [PDF]
The discovery of new dental materials is typically a slow process due to high-dimensionality of the formulation space as well as the multiple competing objectives which must be optimized for a given application.
Ramsey, Issa +2 more
core +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Bayesian Active Learning for Sensitivity Analysis [PDF]
, 2006 Designs of micro electro-mechanical devices need to be robust against fluctuations in mass production. Computer experiments with tens of parameters are used to explore the behavior of the system, and to compute sensitivity measures as expectations over the input distribution.
openaire +2 more sources
Bayesian Active Learning for Classification and Preference Learning
CoRR, 2011 Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute.
Neil Houlsby +3 more
openaire +3 more sources
Active Structure Learning of Bayesian Networks in an Observational Setting
CoRR, 2021 We study active structure learning of Bayesian networks in an observational setting, in which there are external limitations on the number of variable values that can be observed from the same sample. Random samples are drawn from the joint distribution of the network variables, and the algorithm iteratively selects which variables to observe in the ...
Noa Ben-David, Sivan Sabato
openaire +5 more sources
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
BATMAN: BAyesian Target Modelling for Active iNference
, 2020 Active Inference is an emerging framework for designing intelligent agents. In an Active Inference setting, any task is formulated as a variational free energy minimisation problem on a generative probabilistic model.
Koudahl, Magnus, de Vries, A. (Bert)
core +1 more source
Bayesian Active Learning By Distribution Disagreement
CoRRActive Learning (AL) for regression has been systematically under-researched due to the increased difficulty of measuring uncertainty in regression models. Since normalizing flows offer a full predictive distribution instead of a point forecast, they facilitate direct usage of known heuristics for AL like Entropy or Least-Confident sampling.
Thorben Werner, Lars Schmidt-Thieme
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