Bayesian active learning with model selection for spectral experiments [PDF]
Scientific ReportsActive learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments.
Tomohiro Nabika +4 more
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A Bayesian active learning platform for scalable combination drug screens [PDF]
Nature CommunicationsLarge-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations ...
Christopher Tosh +8 more
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Knowledge graph-aided Bayesian active learning for top-K genetic interaction discovery [PDF]
Scientific ReportsIn silico methods for predicting the effects of multi-gene perturbations hold great promise for advancing functional genomics, computational drug discovery, and disease modeling.
Braden Soper +7 more
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Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation
Journal of Imaging, 2021 We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout.
Isah Charles Saidu, Lehel Csató
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Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design [PDF]
Journal of CheminformaticsIn drug discovery, prioritizing compounds for experimental testing is a critical task that can be optimized through active learning by strategically selecting informative molecules. Active learning typically trains models on labeled examples alone, while
Muhammad Arslan Masood +2 more
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Analyzing and supporting mental representations and strategies in solving Bayesian problems [PDF]
Frontiers in PsychologySolving Bayesian problems poses many challenges, such as identifying relevant numerical information, classifying and translating it into mathematical formula language, and forming a mental representation.
Julia Sirock, Markus Vogel, Tina Seufert
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Bayesian active learning with abstention feedbacks [PDF]
Neurocomputing, 2022 Poster presented at 2019 ICML Workshop on Human in the Loop Learning 2019 (non-archival).
Nguyen, Cuong V. +4 more
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A set-based approach to dynamic system design using physics informed neural network
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2022 In the early stage of dynamic system development which has a multi-disciplinary and hierarchical structure, system requirements need to be cascaded down to target values of each component so that engineers can collaborate efficiently and concurrently ...
Kohei SHINTANI +2 more
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An automation system for vehicle driveability evaluation using machine learning
Nihon Kikai Gakkai ronbunshu, 2022 The drivability is one of the important aspects of vehicle dynamic performances. To ensure quality of the drivability performance, comprehensive screening evaluation is necessary by controlling both complicated driver operation and vehicle behavior ...
Hisashi TAJIMA +4 more
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Self-Correcting Bayesian Optimization through Bayesian Active Learning
Advances in neural information processing systems, 2023 Gaussian processes are the model of choice in Bayesian optimization and active learning. Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full potential, and little effort is devoted to finding good hyperparameters in the literature.
Hvarfner, Carl +3 more
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