Results 251 to 260 of about 2,475,679 (305)

Annual Reports to the ESA Council ESA 110th Annual Meeting July, 2025

open access: yes
The Bulletin of the Ecological Society of America, EarlyView.
wiley   +1 more source

Bayesian Model Learning Based on Predictive Entropy

Journal of Logic, Language and Information, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
J. Corander, Pekka Marttinen
semanticscholar   +3 more sources

Efficient Bayesian local model learning for control

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014
Franziska Meier   +2 more
semanticscholar   +3 more sources

Bayesian Distillation of Deep Learning Models

Automation and Remote Control, 2021
The authors present a Bayesian approach to teacher-student networks' knowledge distillation. Knowledge distillation was first proposed by \textit{G. Hinton} et al. in their paper [``Distilling the knowledge in a neural network'', Preprint, \url{arXiv:1503.02531}].
Grabovoy, A. V., Strijov, V. V.
openaire   +1 more source

An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning

IEEE transactions on intelligent transportation systems (Print), 2020
Short-term traffic volume prediction, which can assist road users in choosing appropriate routes and reducing travel time cost, is a significant topic of intelligent transportation system.
Yuanli Gu   +5 more
semanticscholar   +1 more source

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Environmental Science and Technology, 2021
Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation.
Haiping Gao   +12 more
semanticscholar   +1 more source

Learning overhypotheses with hierarchical Bayesian models

Developmental Science, 2007
AbstractInductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired.
Kemp, C., Perfors, A., Tenenbaum, J.
openaire   +3 more sources

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