14.2.15.2 Bayesian Optimization

Chapter Contents (Back)
Bayesian Optimization.

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


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

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:Oct 20, 2021 at 09:45:26