Results 231 to 240 of about 41,144 (258)

Bayesian cognitive diagnosis optimizes personalized learning paths via mediation of cognitive load and Hidden Markov Model state transitions. [PDF]

Front Psychol
Feng Z, Huang K.
europepmc   +1 more source

Handbook of Bayesian Deep Learning

Agostinelli, Claudio, Aitchison, Laurence, Aldea, Emanuel, Allmendinger, Richard, Ament, Sebastian, Anson, Ben, Arbel, Julyan, Bakshy, Eytan, Balandat, Max, Bariletto, Nicola, Bickford Smith, Freddie, Biggio, Battista, Caprio, Michele, Chada, Neil, Chen, Wenlin, Chen, Wenlong, Da Costa, Nathaël, Daheim, Nico, Damianou, Andreas, Deistler, Michael, Dunson, David, Durasov, Nikita, Filippone, Maurizio, Filippozzi, Lucia, Flamich, Gergely, Fortuin, Vincent, Franchi, Gianni, Fumera, Giorgio, Gal, Yarin, Gong, Wenbo, Gu, Yuqi, Guo, Cheng, Hauptvogel, Nick, He, Jiajun, Hennig, Philipp, Hernández-Lobato, José Miguel, Ho, Nhat, Hoffmann, Clara, Hubin, Aliaksandr, Igel, Christian, Jasra, Ajay, Jones, Matt, Klein, Nadja, Laurent, Olivier, Law, Kody J. H., Ledda, Emanuele, Li, Bolian, Li, Yingzhen, Liang, Xinzhu, Macke, Jakob H, Mandt, Stephan, Melo, Luckeciano Carvalho, Miller, Benjamin Kurt, Milsom, Edward, Mlodozeniec, Bruno, Mucsányi, Bálint, Müller, Samuel, Murphy, Kevin, Nalisnick, Eric, Negre Saura, Marcos, Nemeth, Christopher, Nguyen, Huy, Nguyen, Khai, Padhy, Shreyas, Palla, Konstantina, Pan, Wei, Papamarkou, Theodore, Patel, Dhruvesh, Peluso, Alina, Pepin Lehalleur, Simon, Pitas, Konstantinos, Pynadath, Patrick, Rainforth, Tom, Robeyns, Maxime, Roli, Fabio, Roskams-Hieter, Breeshey, Rossi, Simone, Rudner, Tim G. J., Rügamer, David, Sejdinovic, Dino, Seldin, Yevgeny, Sell, Torben, Sharrock, Louis, Singh, Sumeetpal, Sliwa, Joanna, Sommer, Emanuel, Song, Qifan, Tran, Ba-Hien, Turner, Richard E., van der Ouderaa, Tycho F. A., van der Wilk, Mark, Vladimirova, Mariia, Wade, Sara, Wang, Xi, Weber, Tobias, Wei, Susan, Wikle, Christopher K., Wilson, Andrew Gordon, Wimmer, Lisa, Yang, Adam, Yang, Yibo, Yoo, Myungsoo, Zhang, Cheng, Zhang, Likun, Zhang, Ruqi, Zhou, Yuren   +105 more
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Priors in Bayesian Deep Learning: A Review

International Statistical Review, 2022
SummaryWhile the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been
Vincent Fortuin
exaly   +5 more sources

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 Sokolov, Nicholas G Polson
exaly   +4 more sources

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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}].
Andrey V. Grabovoy, Vadim V. Strijov
openaire   +1 more source

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 ...
Shaoyuan Li, Sheng-Jun Huang, Songcan Chen   +2 more
openaire   +2 more sources

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.
openaire   +2 more sources

Integrating Deep Learning and Bayesian Reasoning

2019
Deep 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, Wooi Ping Cheah, Shing Chiang Tan   +2 more
openaire   +3 more sources

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 ...
openaire   +1 more source

A Survey on Bayesian Deep Learning

ACM Computing Surveys, 2021
Yeung Dit Yan
exaly