Results 31 to 40 of about 9,437,008 (290)
Active Learning of Bayesian Probabilistic Movement Primitives [PDF]
IEEE Robotics and Automation Letters, 2021 Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distributions.
Thibaut Kulak +3 more
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Self-Correcting Bayesian Optimization through Bayesian Active Learning
Advances in Neural Information Processing Systems 36, 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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Bayesian active learning with basis functions [PDF]
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011 A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may
Ilya O. Ryzhov, Warren B. Powell
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Adaptive Quadrature Schemes for Bayesian Inference via Active Learning
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 +1 more source
Active and transfer learning with partially Bayesian neural networks for materials and chemicals† [PDF]
Digital DiscoveryActive learning, an iterative process of selecting the most informative data points for exploration, is crucial for efficient characterization of materials and chemicals property space.
Sarah I. Allec, Maxim Ziatdinov
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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 ...
Anestis Tsakmalis +2 more
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Bayesian Learning of Markov Network Structure [PDF]
, 2006 We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina +3 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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Current Directions in Biomedical Engineering, 2021 Intraoperative neurophysiological monitoring (IONM) is an essential tool during numerous surgical interventions to assess and monitor the functional integrity of neural structures at risk.
Kortus Tobias +3 more
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Bayesian Active Learning for Received Signal Strength-Based Visible Light Positioning
IEEE Photonics Journal, 2022 Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength ...
Federico Garbuglia +5 more
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