Results 241 to 250 of about 199,039 (272)

Photovoltaic power interval prediction with conditional error dependency using Bayesian optimized deep learning. [PDF]

Sci Rep
Chen Y, Wang X, Huang R, You G.
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MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning. [PDF]

Front Plant Sci
Mora-Poblete F, Valenzuela-Herrera J, Balach M, Martínez-Araya CJ, Maldonado C.   +4 more
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Some Bayesian nonparametric ideas in (Bayesian) deep learning

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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.
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Deep Bayesian Multimedia Learning

Proceedings of the 28th ACM International Conference on Multimedia, 2020
Deep learning has been successfully developed as a complicated learning process from source inputs to target outputs in presence of multimedia environments. The inference or optimization is performed over an assumed deterministic model with deep structure.
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Bayesian deep learning for single-cell analysis

Nature Methods, 2018
A recent approach for single-cell RNA-sequencing data uses Bayesian deep learning to correct technical artifacts and enable accurate and multifaceted downstream analyses.
Gregory P. Way, Casey S. Greene
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Crowdsourcing aggregation with deep Bayesian learning

Science China Information Sciences, 2021
In this study, we consider a crowdsourcing classification problem in which labeling information from crowds is aggregated to infer latent true labels. We propose a fully Bayesian deep generative crowdsourcing model (BayesDGC), which combines the strength of deep neural networks (DNNs) on automatic representation learning and the interpretable ...
Shao-Yuan Li, Sheng-Jun Huang, Songcan Chen   +2 more
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Bayesian deep learning for system identification

2022
Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent neural ...
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Parallelised Bayesian optimisation for deep learning

2022
Η εκπαίδευση σε βαθιά νευρωνικά δίκτυα (ΒΝΔ) είναι μια απαραίτητη διαδικασία στη μηχανική μάθηση. Η διαδικασία εκπαίδευσης των ΒΝΔ στοχεύει στη βελτιστοποίηση των τιμών των παραμέτρων του δικτύου, που συχνά βασίζεται στην παράγωγο των λογαριθμικών πιθανοτήτων των παραμέτρων.
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Deep Bayesian Mining, Learning and Understanding

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
This tutorial addresses the advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation ...
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