Efficient sampling-based Bayesian Active Learning for synaptic characterization. [PDF]
PLoS Computational Biology, 2023 Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL
Camille Gontier +4 more
doaj +6 more sources
Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning [PDF]
Nature Communications, 2021 Characterizing an unknown, complex system, like an accelerator, in multi-dimensional space is a challenging task. Here the authors report a Bayesian active learning method - Constrained Proximal Bayesian Exploration - for the characterization of a ...
Ryan Roussel +8 more
doaj +2 more sources
On-the-fly closed-loop materials discovery via Bayesian active learning [PDF]
Nature Communications, 2020 Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase-
A. Gilad Kusne +15 more
doaj +2 more sources
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning [PDF]
Frontiers in Physiology, 2021 Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification.
Md Shakil Zaman +7 more
doaj +2 more sources
Contrast response function estimation with nonparametric Bayesian active learning. [PDF]
J Vis, 2023 AbstractMultidimensional psychometric functions can typically be estimated nonparametrically for greater accuracy or parametrically for greater efficiency. By recasting the estimation problem from regression to classification, however, powerful machine learning tools can be leveraged to provide an adjustable balance between accuracy and efficiency ...
Marticorena DCP +8 more
europepmc +5 more sources
A Bayesian Framework for Active Learning [PDF]
The 2010 International Joint Conference on Neural Networks (IJCNN), 2013 Copyright © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating
Everson, Richard M. +2 more
core +3 more sources
Efficient structure learning of gene regulatory networks with Bayesian active learning [PDF]
BMC BioinformaticsBackground Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions.
Dániel Sándor, Péter Antal
doaj +2 more sources
Adaptive Quadrature Schemes for Bayesian Inference via Active Learning [PDF]
IEEE Access, 2020 We propose novel adaptive quadrature schemes based on an active learning procedure. We consider an interpolative approach for building a surrogate posterior density, combining it with Monte Carlo sampling methods and other quadrature rules.
Fernando Llorente Fernandez +4 more
doaj +4 more sources
Active Bayesian perception and reinforcement learning [PDF]
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013 In a series of papers, we have formalized an active Bayesian perception approach for robotics based on recent progress in understanding animal perception.
Lepora, N.F. +3 more
core +8 more sources
Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction [PDF]
Nature CommunicationsUltrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems.
Fuhao Ji +15 more
doaj +2 more sources