Results 21 to 30 of about 190,737 (267)
Noisy Bayesian active learning [PDF]
2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012 39 pages (one-column), 5 figures, submitted to IEEE Transactions on Information ...
Naghshvar, Mohammad +2 more
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npj Computational Materials, 2023 Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
Yu Xie +5 more
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Active Inference Integrated With Imitation Learning for Autonomous Driving
IEEE Access, 2022 Classical imitation learning methods suffer substantially from the learning hierarchical policies when the imitative agent faces an unobserved state by the expert agent.
Sheida Nozari +5 more
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Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors
Frontiers in Digital Health, 2021 Pure-tone audiometry—the process of estimating a person's hearing threshold from “audible” and “inaudible” responses to tones of varying frequency and intensity—is the basis for diagnosing and quantifying hearing loss.
Marco Cox, Bert de Vries, Bert de Vries
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Active learning of continuous-time Bayesian networks through interventions* [PDF]
Journal of Statistical Mechanics: Theory and Experiment, 2021 Abstract We consider the problem of learning structures and parameters of continuous-time Bayesian networks (CTBNs) from time-course data under minimal experimental resources. In practice, the cost of generating experimental data poses a bottleneck, especially in the natural and social sciences.
Linzner, D., Koeppl, H.
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Bayesian Active Learning with Fully Bayesian Gaussian Processes
, 2022 The bias-variance trade-off is a well-known problem in machine learning that only gets more pronounced the less available data there is. In active learning, where labeled data is scarce or difficult to obtain, neglecting this trade-off can cause inefficient and non-optimal querying, leading to unnecessary data labeling.
Riis, Christoffer +4 more
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Constrained Bayesian Active Learning of a Linear Classifier [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle ...
Tsakmalis, Anestis +2 more
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Prediction-Oriented Bayesian Active Learning
, 2023 Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BALD score. We highlight that this can be suboptimal from the perspective of predictive performance.
Smith, Freddie Bickford +5 more
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Targeted Active Learning for Bayesian Decision-Making [PDF]
Transactions on Machine Learning Research, 2021 Peer ...
Filstroff, Louis +5 more
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Strategies for Using a Spatial Method to Promote Active Learning of Probability Concepts
Journal of Statistics and Data Science Education, 2021 We developed and tested strategies for using spatial representations to help students understand core probability concepts, including the multiplication rule for computing a joint probability from a marginal and conditional probability, interpreting an ...
Jeffrey J. Starns +3 more
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