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
Tung, F.[Frederick],
Mori, G.[Greg],
Deep Neural Network Compression by In-Parallel Pruning-Quantization,
PAMI(42), No. 3, March 2020, pp. 568-579.
IEEE DOI
2002
BibRef
Earlier:
CLIP-Q: Deep Network Compression Learning by In-parallel
Pruning-Quantization,
CVPR18(7873-7882)
IEEE DOI
1812
Quantization (signal), Image coding, Neural networks,
Visualization, Training, Convolution, Network architecture,
Bayesian optimization.
Training, Task analysis, Optimization
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
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 .