Suau, P.[Pablo],
Escolano, F.[Francisco],
Bayesian optimization of the scale saliency filter,
IVC(26), No. 9, 1 September 2008, pp. 1207-1218.
Elsevier DOI
0806
BibRef
Earlier:
Exploiting Information Theory for Filtering the Kadir Scale-Saliency
Detector,
IbPRIA07(II: 146-153).
Springer DOI
0706
Scale saliency detector; Information Theory
BibRef
Suau, P.[Pablo],
Escolano, F.[Francisco],
Entropy Estimation and Multi-Dimensional Scale Saliency,
ICPR10(678-681).
IEEE DOI
1008
BibRef
Earlier:
A New Feasible Approach to Multi-dimensional Scale Saliency,
ACIVS09(77-88).
Springer DOI
0909
BibRef
Earlier:
Multi-dimensional Scale Saliency Feature Extraction Based on Entropic
Graphs,
ISVC08(II: 170-180).
Springer DOI
0812
BibRef
Burduk, R.[Robert],
Classifier fusion with interval-valued weights,
PRL(34), No. 14, 2013, pp. 1623-1629.
Elsevier DOI
1308
BibRef
Earlier:
Probability Error in Bayes Optimal Classifier with Intuitionistic Fuzzy
Observations,
ICIAR09(359-368).
Springer DOI
0907
Classifier fusion
BibRef
Shahriari, B.,
Swersky, K.,
Wang, Z.,
Adams, R.P.,
de Freitas, N.,
Taking the Human Out of the Loop: A Review of Bayesian Optimization,
PIEEE(104), No. 1, January 2016, pp. 148-175.
IEEE DOI
1601
Bayes methods
BibRef
Cui, H.[Hua],
Bai, J.[Jie],
A new hyperparameters optimization method for convolutional neural
networks,
PRL(125), 2019, pp. 828-834.
Elsevier DOI
1909
Convolutional neural networks, Hyperparameters optimization,
Multilevel evolutionary optimization, Bayesian optimization
BibRef
Tran, N.[Ngoc],
Schneider, J.G.[Jean-Guy],
Weber, I.[Ingo],
Qin, A.K.,
Hyper-parameter optimization in classification: To-do or not-to-do,
PR(103), 2020, pp. 107245.
Elsevier DOI
2005
Hyper-parameter optimization, Framework, Bayesian optimization,
Machine learning, Incremental learning
BibRef
Ma, X.C.[Xing-Chen],
Blaschko, M.B.[Matthew B.],
Additive Tree-Structured Conditional Parameter Spaces in Bayesian
Optimization: A Novel Covariance Function and a Fast Implementation,
PAMI(43), No. 9, September 2021, pp. 3024-3036.
IEEE DOI
2108
Optimization, Additives, Mathematical model, Linear programming,
Bayes methods, Neural networks, Data models,
parameter learning
BibRef
Ma, X.C.[Xing-Chen],
Rannen-Triki, A.,
Berman, M.,
Sagonas, C.,
Cali, J.,
Blaschko, M.B.,
A Bayesian Optimization Framework for Neural Network Compression,
ICCV19(10273-10282)
IEEE DOI
2004
approximation theory, Bayes methods, data compression, neural nets,
optimisation, neural network compression, Training
BibRef
Nguyen, D.T.[Duy Thanh],
Kim, H.[Hyun],
Lee, H.J.[Hyuk-Jae],
Layer-Specific Optimization for Mixed Data Flow With Mixed Precision
in FPGA Design for CNN-Based Object Detectors,
CirSysVideo(31), No. 6, June 2021, pp. 2450-2464.
IEEE DOI
2106
Hardware, Memory management, Optimization, Quantization (signal),
Throughput, Field programmable gate arrays, Organizations,
Bayesian optimization
BibRef
Sevilla-Salcedo, C.[Carlos],
Gómez-Verdejo, V.[Vanessa],
Olmos, P.M.[Pablo M.],
Sparse semi-supervised heterogeneous interbattery bayesian analysis,
PR(120), 2021, pp. 108141.
Elsevier DOI
2109
Bayesian model, Canonical correlation analysis,
Principal component analysis, Factor analysis, Multi-task
BibRef
Yin, C.R.[Chao-Ran],
Hao, C.P.[Cheng-Peng],
Orlando, D.[Danilo],
Hou, C.H.[Chao-Huan],
Learning Strategies for the Interference Covariance Structure Based
on a Bayesian Approach,
SPLetters(29), 2022, pp. 1182-1186.
IEEE DOI
2205
Covariance matrices, Symmetric matrices, Detectors, Bayes methods,
Clutter, Uncertainty, Silicon, Bayesian framework, symmetric spectrum
BibRef
Xie, J.Y.[Ji-Yang],
Ma, Z.Y.[Zhan-Yu],
Lei, J.J.[Jian-Jun],
Zhang, G.Q.[Guo-Qiang],
Xue, J.H.[Jing-Hao],
Tan, Z.H.[Zheng-Hua],
Guo, J.[Jun],
Advanced Dropout: A Model-Free Methodology for Bayesian Dropout
Optimization,
PAMI(44), No. 9, September 2022, pp. 4605-4625.
IEEE DOI
2208
Training, Bayes methods, Standards, Gaussian distribution,
Adaptation models, Stochastic processes, Neural networks,
stochastic gradient variational Bayes
BibRef
Puerto-Santana, C.[Carlos],
Larrañaga, P.[Pedro],
Bielza, C.[Concha],
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models,
PAMI(44), No. 9, September 2022, pp. 4642-4658.
IEEE DOI
2208
Hidden Markov models, Markov processes, Graphical models,
Bayes methods, Probabilistic logic, Mathematical model,
Yule-Walker equations
BibRef
Maskell, S.[Simon],
Zhou, Y.F.[Yi-Fan],
Mira, A.[Antonietta],
Control Variates for Constrained Variables,
SPLetters(29), 2022, pp. 2333-2337.
IEEE DOI
2212
Monte Carlo methods, Standards, Random variables, Bayes methods,
Aerospace electronics, Probabilistic logic, Markov processes,
zero variance
BibRef
Gu, J.,
Zhao, J.,
Jiang, X.,
Zhang, B.,
Liu, J.,
Guo, G.,
Ji, R.,
Bayesian Optimized 1-Bit CNNs,
ICCV19(4908-4916)
IEEE DOI
2004
Bayes methods, convolutional neural nets,
feature extraction, image classification, Indexes
BibRef
Semage, B.L.[Buddhika Laknath],
Karimpanal, T.G.[Thommen George],
Rana, S.[Santu],
Venkatesh, S.[Svetha],
Fast Model-based Policy Search for Universal Policy Networks,
ICPR22(2314-2320)
IEEE DOI
2212
Adaptation models, Reinforcement learning, Gaussian processes,
Numerical models, Bayes methods, Physics
BibRef
Semage, B.L.[Buddhika Laknath],
Karimpanal, T.G.[Thommen George],
Rana, S.[Santu],
Venkatesh, S.[Svetha],
Uncertainty Aware System Identification with Universal Policies,
ICPR22(2321-2327)
IEEE DOI
2212
Training, Uncertainty, Parameter estimation, Grounding, Estimation,
System identification
BibRef
Joy, T.T.,
Rana, S.,
Gupta, S.,
Venkatesh, S.,
Hyperparameter tuning for big data using Bayesian optimisation,
ICPR16(2574-2579)
IEEE DOI
1705
Bayes methods, Big Data, Data models, Gaussian processes,
Noise measurement, Optimization, Tuning
BibRef
Martinez, J.[Julieta],
Little, J.J.[James J.],
de Freitas, N.[Nando],
Bayesian Optimization with an Empirical Hardness Model for
approximate Nearest Neighbour Search,
WACV14(588-595)
IEEE DOI
1406
Artificial neural networks
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Archetypal Analysis .