Results 241 to 250 of about 201,968 (266)
Contrastive Bayesian Analysis for Deep Metric Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 Accepted by IEEE Transactions on Pattern Analysis and Machine ...
Shichao Kan, Zhiquan He, Yi-Gang Cen
exaly +4 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Bayesian Distillation of Deep Learning Models
Automation and Remote Control, 2021The 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
Deep Learning: A Bayesian Perspective
Bayesian Analysis, 2017 Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning.
Vadim O Sokolov
exaly +4 more sources
Deep Bayesian Multimedia Learning
Proceedings of the 28th ACM International Conference on Multimedia, 2020Deep 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.
openaire +1 more source
Bayesian deep learning for single-cell analysis
Nature Methods, 2018A 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
openaire +2 more sources
Crowdsourcing aggregation with deep Bayesian learning
Science China Information Sciences, 2021In 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 +2 more
openaire +1 more source
Bayesian deep learning for system identification
2022Applying 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 ...
openaire +5 more sources
Parallelised Bayesian optimisation for deep learning
2022Η 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.
openaire +1 more source
Deep Bayesian Mining, Learning and Understanding
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019This 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 ...
openaire +1 more source
Integrating Deep Learning and Bayesian Reasoning
2019Deep learning (DL) is an excellent function estimator which has amazing result on perception tasks such as visualization recognition and text recognition. But, its inner architecture acts as a black box, because the users cannot understand why such decisions are made.
Sin, Yin Tan +2 more
openaire +2 more sources