Results 41 to 50 of about 190,737 (267)
Unscented Bayesian Optimization for Safe Robot Grasping [PDF]
, 2016 We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy.
Bernardino, Alexandre +3 more
core +2 more sources
Bayesian active learning for electromagnetic structure design [PDF]
2020 14th European Conference on Antennas and Propagation (EuCAP), 2020 A novel design framework based on Bayesian active learning is presented in this contribution. The proposed approach allows one to identify a set of design configurations satisfying the chosen specification. In particular, the entropy search-based active learning strategy, which relies on a Gaussian Process model, is able to minimize the number of time ...
Qing, Jixiang +4 more
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Implementation Science Communications, 2022 Background Incorporating physical movement in the teaching of academic content (active learning) is a promising approach to improve children’s health and academic performance.
Timothy J. Walker +4 more
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Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems
IEEE Access, 2022 Deep learning technologies, due to their advanced pattern extraction and recognition of high-dimensional data, have been widely adopted into multisensor-based fire detection systems.
Young-Jin Kim, Won-Tae Kim
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Automatic evaluation system for vehicle Adaptive cruise control using Bayesian Active Learning
Nihon Kikai Gakkai ronbunshuAdaptive cruise control (ACC) is one of the critical elements of vehicle performance in the market. To ensure the quality of ACC performance, comprehensive evaluations that control both complex test scenarios that reproduce market driving conditions and ...
Mikoto YAMAMOTO +4 more
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Synthetic Spatial Foraging With Active Inference in a Geocaching Task
Frontiers in Neuroscience, 2022 Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we
Victorita Neacsu +3 more
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Bayesian Generative Active Deep Learning
, 2019 Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled.
Tran, Toan +3 more
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Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning [PDF]
Monthly Notices of the Royal Astronomical Society, 2019 ABSTRACT We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label.
Mike Walmsley +12 more
openaire +5 more sources
Goal-Driven Dynamics Learning via Bayesian Optimization
, 2017 Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the
Bansal, Somil +4 more
core +1 more source
Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead!
Engineering, 2023 By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted.
Yannick Ureel +6 more
doaj +1 more source