14.2.16.2 Bayesian Optimization

Chapter Contents (Back)
Bayesian Optimization.
See also Bayesian Neural Networks.

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


Balabanov, O.[Oleksandr], Mehlig, B.[Bernhard], Linander, H.[Hampus],
Bayesian Posterior Approximation With Stochastic Ensembles,
CVPR23(13701-13711)
IEEE DOI 2309
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 .


Last update:Apr 10, 2024 at 09:54:40