14.5.8.3 Neural Architecture, Neural Architecture Search

Chapter Contents (Back)
Neural Networks. Neural Architecture Search. See also Convolutional Neural Networks, Design, Implementation Issues.

Banarse, D.S., Duller, A.W.G.,
Deformation Invariant Visual Object Recognition: Experiments with a Self-Organizing Neural Architecture,
NeurCompApp(6), No. 2, 1997, pp. 79-90. 9801
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Shih, F.Y.[Frank Y.], Moh, J.[Jenlong], Chang, F.C.[Fu-Chun],
A new art-based neural architecture for pattern classification and image enhancement without prior knowledge,
PR(25), No. 5, May 1992, pp. 533-542.
Elsevier DOI 0401
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Taylor, J.G., Hartley, M., Taylor, N., Panchev, C., Kasderidis, S.,
A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning,
IVC(27), No. 11, 2 October 2009, pp. 1641-1657.
Elsevier DOI 0909
Dorsal and ventral vision; Object representations; Dopamine as reward; TD learning BibRef

Iakymchuk, T.[Taras], Rosado-Munoz, A.[Alfredo], Guerrero-Martinez, J.[Juan], Bataller-Mompean, M.[Manuel], Frances-Villora, J.[Jose],
Simplified spiking neural network architecture and STDP learning algorithm applied to image classification,
JIVP(2015), No. 1, 2015, pp. 4.
DOI Link 1503
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Lerouge, J., Herault, R., Chatelain, C., Jardin, F., Modzelewski, R.,
IODA: An input/output deep architecture for image labeling,
PR(48), No. 9, 2015, pp. 2847-2858.
Elsevier DOI 1506
Deep learning architectures BibRef

Chen, Y.S.[Yu-Shi], Zhu, K.Q.[Kai-Qiang], Zhu, L.[Lin], He, X.[Xin], Ghamisi, P.[Pedram], Benediktsson, J.A.[Jón Atli],
Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification,
GeoRS(57), No. 9, September 2019, pp. 7048-7066.
IEEE DOI 1909
Feature extraction, Deep learning, Hyperspectral imaging, Convolution, Training, Convolutional neural network (CNN), neural architecture search (NAS) BibRef

Jaafra, Y.[Yesmina], Laurent, J.L.[Jean Luc], Deruyver, A.[Aline], Naceur, M.S.[Mohamed Saber],
Reinforcement learning for neural architecture search: A review,
IVC(89), 2019, pp. 57-66.
Elsevier DOI 1909
Reinforcement learning, Convolutional neural networks, Neural Architecture Search, AutoML BibRef

Sun, Y., Xue, B., Zhang, M., Yen, G.G., Lv, J.,
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification,
Cyber(50), No. 9, September 2020, pp. 3840-3854.
IEEE DOI 2008
Computer architecture, Tuning, Genetic algorithms, Evolutionary computation, Manuals, Genetics, Evolution (biology), neural-network architecture optimization BibRef

Dong, H., Zou, B., Zhang, L., Zhang, S.,
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification,
GeoRS(58), No. 9, September 2020, pp. 6362-6375.
IEEE DOI 2008
Computer architecture, Personal digital assistants, Deep learning, Search problems, Neural networks, polarimetric synthetic aperture radar (PolSAR) classification BibRef


Wen, W.[Wei], Liu, H.X.[Han-Xiao], Chen, Y.R.[Yi-Ran], Li, H.[Hai], Bender, G.[Gabriel], Kindermans, P.J.[Pieter-Jan],
Neural Predictor for Neural Architecture Search,
ECCV20(XXIX: 660-676).
Springer DOI 2010
BibRef

Vahdat, A., Mallya, A., Liu, M., Kautz, J.,
UNAS: Differentiable Architecture Search Meets Reinforcement Learning,
CVPR20(11263-11272)
IEEE DOI 2008
Computer architecture, Search problems, DNA, Linear programming, Task analysis, Estimation, Loss measurement BibRef

Murdock, C.[Calvin], Lucey, S.[Simon],
Dataless Model Selection With the Deep Frame Potential,
CVPR20(11254-11262)
IEEE DOI 2008
Dictionaries, Sparse representation, Robustness, Coherence, Neural networks, Computer architecture, Machine learning BibRef

Berman, M.[Maxim], Pishchulin, L.[Leonid], Xu, N.[Ning], Blaschko, M.B.[Matthew B.], Medioni, G.[Gérard],
AOWS: Adaptive and Optimal Network Width Search With Latency Constraints,
CVPR20(11214-11223)
IEEE DOI 2008
Training, Computational modeling, Computer architecture, Task analysis, Neural networks, Hardware, Measurement BibRef

Chen, Z.S.[Zheng-Su], Niu, J.W.[Jian-Wei], Xie, L.X.[Ling-Xi], Liu, X.F.[Xue-Feng], Wei, L.H.[Long-Hui], Tian, Q.[Qi],
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio,
CVPR20(10655-10664)
IEEE DOI 2008
Training, Computer architecture, Neural networks, Channel estimation, Pipelines, Computer vision, Standards BibRef

Radosavovic, I.[Ilija], Kosaraju, R.P.[Raj Prateek], Girshick, R.[Ross], He, K.[Kaiming], Dollár, P.[Piotr],
Designing Network Design Spaces,
CVPR20(10425-10433)
IEEE DOI 2008
Computational modeling, Manuals, Tools, Sociology, Statistics, Training, Visualization BibRef

Zoran, D.[Daniel], Chrzanowski, M.[Mike], Huang, P.S.[Po-Sen], Gowal, S.[Sven], Mott, A.[Alex], Kohli, P.[Pushmeet],
Towards Robust Image Classification Using Sequential Attention Models,
CVPR20(9480-9489)
IEEE DOI 2008
Augment NN with attention model. Robustness, Computational modeling, Adaptation models, Brain modeling, Biological system modeling, Training BibRef

Huang, L.[Lei], Zhao, L.[Lei], Zhou, Y.[Yi], Zhu, F.[Fan], Liu, L.[Li], Shao, L.[Ling],
An Investigation Into the Stochasticity of Batch Whitening,
CVPR20(6438-6447)
IEEE DOI 2008
Training, Principal component analysis, Covariance matrices, Standardization, Optimization, Sociology BibRef

Bender, G., Liu, H., Chen, B., Chu, G., Cheng, S., Kindermans, P., Le, Q.V.,
Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS,
CVPR20(14311-14320)
IEEE DOI 2008
Computer architecture, Search problems, Google, Inference algorithms, Task analysis, Training BibRef

Kim, E., Kang, W.Y., On, K., Heo, Y., Zhang, B.,
Hypergraph Attention Networks for Multimodal Learning,
CVPR20(14569-14578)
IEEE DOI 2008
Semantics, Visualization, Task analysis, Knowledge discovery, Message passing, Computational modeling, Biological neural networks BibRef

Lin, R., Liu, W., Liu, Z., Feng, C., Yu, Z., Rehg, J.M., Xiong, L., Song, L.,
Regularizing Neural Networks via Minimizing Hyperspherical Energy,
CVPR20(6916-6925)
IEEE DOI 2008
Neurons, Biological neural networks, Training, Optimization, Task analysis, Testing, Linear programming BibRef

Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., Wu, B., Yu, M., Xu, T., Chen, K., Vajda, P., Gonzalez, J.E.,
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions,
CVPR20(12962-12971)
IEEE DOI 2008
DNA, Computer architecture, Convolution, Neural networks, Training, Computational efficiency, Space exploration BibRef

Mozejko, M., Latkowski, T., Treszczotko, L., Szafraniuk, M., Trojanowski, K.,
Superkernel Neural Architecture Search for Image Denoising,
NTIRE20(2002-2011)
IEEE DOI 2008
Training, Task analysis, Image denoising, Kernel, Memory management, Graphics processing units BibRef

Zhang, L.L., Yang, Y., Jiang, Y., Zhu, W., Liu, Y.,
Fast Hardware-Aware Neural Architecture Search,
EDLCV20(2959-2967)
IEEE DOI 2008
Hardware, Computer architecture, Graphics processing units, Hurricanes, Training, Measurement BibRef

Gao, Y., Bai, H., Jie, Z., Ma, J., Jia, K., Liu, W.,
MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning,
CVPR20(11540-11549)
IEEE DOI 2008
Task analysis, Computer architecture, Entropy, Neural networks, Feature extraction, Semantics, Convolution BibRef

Zhou, D., Zhou, X., Zhang, W., Loy, C.C., Yi, S., Zhang, X., Ouyang, W.,
EcoNAS: Finding Proxies for Economical Neural Architecture Search,
CVPR20(11393-11401)
IEEE DOI 2008
Training, Computer architecture, Reliability, Graphics processing units, Acceleration, Computer vision, Measurement BibRef

Zheng, X., Ji, R., Wang, Q., Ye, Q., Li, Z., Tian, Y., Tian, Q.,
Rethinking Performance Estimation in Neural Architecture Search,
CVPR20(11353-11362)
IEEE DOI 2008
Computer architecture, Estimation, Microprocessors, Training, Optimization, Search problems, Learning (artificial intelligence) BibRef

Fang, J., Sun, Y., Zhang, Q., Li, Y., Liu, W., Wang, X.,
Densely Connected Search Space for More Flexible Neural Architecture Search,
CVPR20(10625-10634)
IEEE DOI 2008
Routing, Computer architecture, Tensile stress, Estimation, Approximation algorithms, Spatial resolution, Microprocessors BibRef

Li, Y., Jin, X., Mei, J., Lian, X., Yang, L., Xie, C., Yu, Q., Zhou, Y., Bai, S., Yuille, A.L.,
Neural Architecture Search for Lightweight Non-Local Networks,
CVPR20(10294-10303)
IEEE DOI 2008
Computer architecture, Neural networks, Computational modeling, Task analysis, Graphics processing units, Mobile handsets, Computational complexity BibRef

Hu, S., Xie, S., Zheng, H., Liu, C., Shi, J., Liu, X., Lin, D.,
DSNAS: Direct Neural Architecture Search Without Parameter Retraining,
CVPR20(12081-12089)
IEEE DOI 2008
Task analysis, Computer architecture, Optimization, Training, Search problems, Measurement, Machine learning BibRef

Atzmon, M.[Matan], Lipman, Y.[Yaron],
SAL: Sign Agnostic Learning of Shapes From Raw Data,
CVPR20(2562-2571)
IEEE DOI 2008
Surface reconstruction, Shape, Neural networks, Interpolation, Mathematical model, Training BibRef

Li, C., Peng, J., Yuan, L., Wang, G., Liang, X., Lin, L., Chang, X.,
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation,
CVPR20(1986-1995)
IEEE DOI 2008
Computer architecture, Network architecture, Knowledge engineering, Training, DNA, Convergence, Feature extraction BibRef

Tang, Y.H.[Ye-Hui], Wang, Y.H.[Yun-He], Xu, Y.X.[Yi-Xing], Chen, H.T.[Han-Ting], Shi, B.X.[Bo-Xin], Xu, C.[Chao], Xu, C.J.[Chun-Jing], Tian, Q.[Qi], Xu, C.[Chang],
A Semi-Supervised Assessor of Neural Architectures,
CVPR20(1807-1816)
IEEE DOI 2008
Computer architecture, Training, Task analysis, Microprocessors, Optimization, Neural networks, Feature extraction BibRef

Li, Z., Xi, T., Deng, J., Zhang, G., Wen, S., He, R.,
GP-NAS: Gaussian Process Based Neural Architecture Search,
CVPR20(11930-11939)
IEEE DOI 2008
Computer architecture, Correlation, Kernel, Training, Task analysis, Network architecture, Mutual information BibRef

Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., Zhang, Y.,
NAS-FCOS: Fast Neural Architecture Search for Object Detection,
CVPR20(11940-11948)
IEEE DOI 2008
Object detection, Computer architecture, Search problems, Task analysis, Decoding, Feature extraction, Detectors BibRef

He, C., Ye, H., Shen, L., Zhang, T.,
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation,
CVPR20(11990-11999)
IEEE DOI 2008
Mathematical model, Training, Computer architecture, Optimization methods, Training data, Neural networks BibRef

Tao, Y., Ma, R., Shyu, M., Chen, S.,
Challenges in Energy-Efficient Deep Neural Network Training with FPGA,
LPCV20(1602-1611)
IEEE DOI 2008
Field programmable gate arrays, Computational modeling, Training, Hardware, Neural networks, Machine learning, Graphics processing units BibRef

Liu, P., Wu, B., Ma, H., Seok, M.,
MemNAS: Memory-Efficient Neural Architecture Search With Grow-Trim Learning,
CVPR20(2105-2113)
IEEE DOI 2008
Memory management, Neural networks, Correlation, Performance evaluation, Computational modeling BibRef

Gao, C., Chen, Y., Liu, S., Tan, Z., Yan, S.,
AdversarialNAS: Adversarial Neural Architecture Search for GANs,
CVPR20(5679-5688)
IEEE DOI 2008
Computer architecture, Generators, Task analysis, Convolution, Generative adversarial networks, Computer vision BibRef

Li, G.H.[Guo-Hao], Qian, G.C.[Guo-Cheng], Delgadillo, I.C.[Itzel C.], Müller, M.[Matthias], Thabet, A.[Ali], Ghanem, B.[Bernard],
SGAS: Sequential Greedy Architecture Search,
CVPR20(1617-1627)
IEEE DOI 2008
Neural Architecture Search. Computer architecture, Correlation, Search problems, Task analysis, Optimization, Computational efficiency, Microprocessors BibRef

Gong, X., Chang, S., Jiang, Y., Wang, Z.,
AutoGAN: Neural Architecture Search for Generative Adversarial Networks,
ICCV19(3223-3233)
IEEE DOI 2004
Code, Generative Adversarial Network.
WWW Link. image classification, image segmentation, neural nets, neural architecture search, generative adversarial networks, Prediction algorithms BibRef

Yan, S., Fang, B., Zhang, F., Zheng, Y., Zeng, X., Zhang, M., Xu, H.,
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking,
NeruArch19(1942-1950)
IEEE DOI 2004
Code, Neural Netowrks.
WWW Link. learning (artificial intelligence), neural net architecture, multilevel architecture, flexible network architectures, Hierarchical Masking BibRef

Wu, B.C.[Bi-Chen], Dai, X.L.[Xiao-Liang], Zhang, P.Z.[Pei-Zhao], Wang, Y.H.[Yang-Han], Sun, F.[Fei], Wu, Y.M.[Yi-Ming], Tian, Y.D.[Yuan-Dong], Vajda, P.[Peter], Jia, Y.Q.[Yang-Qing], Keutzer, K.[Kurt],
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search,
CVPR19(10726-10734).
IEEE DOI 2002
BibRef

Cui, J., Chen, P., Li, R., Liu, S., Shen, X., Jia, J.,
Fast and Practical Neural Architecture Search,
ICCV19(6508-6517)
IEEE DOI 2004
learning (artificial intelligence), neural nets, FPNAS, search process, bi-level optimization problem, design networks, Network architecture BibRef

Bashivan, P.[Pouya], Tensen, M.[Mark], Dicarlo, J.[James],
Teacher Guided Architecture Search,
ICCV19(5319-5328)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), neural net architecture, Network architecture BibRef

Zheng, X., Ji, R., Tang, L., Zhang, B., Liu, J., Tian, Q.,
Multinomial Distribution Learning for Effective Neural Architecture Search,
ICCV19(1304-1313)
IEEE DOI 2004
Code, Neural Networks.
WWW Link. graphics processing units, learning (artificial intelligence), neural nets, Search problems BibRef

Zhu, H., An, Z., Yang, C., Xu, K., Zhao, E., Xu, Y.,
EENA: Efficient Evolution of Neural Architecture,
NeruArch19(1891-1899)
IEEE DOI 2004
learning (artificial intelligence), neural net architecture, search problems, crossover operations, evolution process, guidance of experience gained BibRef

Tan, M.X.[Ming-Xing], Chen, B.[Bo], Pang, R.[Ruoming], Vasudevan, V.[Vijay], Sandler, M.[Mark], Howard, A.[Andrew], Le, Q.V.[Quoc V.],
MnasNet: Platform-Aware Neural Architecture Search for Mobile,
CVPR19(2815-2823).
IEEE DOI 2002
BibRef

Dong, X.Y.[Xuan-Yi], Yang, Y.[Yi],
Searching for a Robust Neural Architecture in Four GPU Hours,
CVPR19(1761-1770).
IEEE DOI 2002
BibRef

Dong, X.Y.[Xuan-Yi], Yang, Y.[Yi],
One-Shot Neural Architecture Search via Self-Evaluated Template Network,
ICCV19(3680-3689)
IEEE DOI 2004
image sampling, knowledge based systems, learning (artificial intelligence), neural net architecture, BibRef

Rashwan, A., Kalra, A., Poupart, P.,
Matrix Nets: A New Deep Architecture for Object Detection,
NeruArch19(2025-2028)
IEEE DOI 2004
learning (artificial intelligence), neural net architecture, object detection, Matrix Nets, deep architecture, object detection, neural architecture BibRef

Cheng, H., Zhang, T., Yang, Y., Yan, F., Teague, H., Chen, Y., Li, H.,
MSNet: Structural Wired Neural Architecture Search for Internet of Things,
NeruArch19(2033-2036)
IEEE DOI 2004
convolutional neural nets, Internet of Things, learning (artificial intelligence), mobile computing, neural architecture search BibRef

Chen, H.T.[Han-Ting], Wang, Y.[Yunhe], Xu, C.[Chang], Yang, Z.H.[Zhao-Hui], Liu, C.J.[Chuan-Jian], Shi, B.X.[Bo-Xin], Xu, C.J.[Chun-Jing], Xu, C.[Chao], Tian, Q.[Qi],
Data-Free Learning of Student Networks,
ICCV19(3513-3521)
IEEE DOI 2004
computer vision, convolutional neural nets, learning (artificial intelligence), neural net architecture, Knowledge engineering BibRef

Liu, C.X.[Chen-Xi], Zoph, B.[Barret], Neumann, M.[Maxim], Shlens, J.[Jonathon], Hua, W.[Wei], Li, L.J.[Li-Jia], Fei-Fei, L.[Li], Yuille, A.L.[Alan L.], Huang, J.[Jonathan], Murphy, K.[Kevin],
Progressive Neural Architecture Search,
ECCV18(I: 19-35).
Springer DOI 1810
New method for learning CNN BibRef

Srinivas, S.[Suraj], Babu, V.[Venkatesh],
Learning Neural Network Architectures using Backpropagation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Hasegawa, R.[Ryoma], Hotta, K.[Kazuhiro],
PLSNet: A simple network using Partial Least Squares regression for image classification,
ICPR16(1601-1606)
IEEE DOI 1705
Convolution, Databases, Feature extraction, Image classification, Network architecture, Principal component analysis, Training, Convolutional Neural Network, Deep Learning, PCANet, PLSNet, Partial Least Squares Regression, Stacked, PLS BibRef

Espinal, A.[Andrés], Carpio, M.[Martín], Ornelas, M.[Manuel], Puga, H.[Héctor], Melín, P.[Patricia], Sotelo-Figueroa, M.[Marco],
Developing Architectures of Spiking Neural Networks by Using Grammatical Evolution Based on Evolutionary Strategy,
MCPR14(71-80).
Springer DOI 1407
BibRef

Elliman, D.G.[David G.], Youssef, S.M.[Sherin M.],
Contextual Swarm-Based Multi-layered Lattices: A New Architecture for Contextual Pattern Recognition,
DAS04(496-507).
Springer DOI 0505
BibRef

Yang, Z., Wang, Y., Chen, X., Shi, B., Xu, C., Xu, C., Tian, Q., Xu, C.,
CARS: Continuous Evolution for Efficient Neural Architecture Search,
CVPR20(1826-1835)
IEEE DOI 2008
Optimization, Nickel, Computer architecture, Network architecture, Sorting, Training, Automobiles BibRef

Chen, Y.K.[Yu-Kang], Meng, G.F.[Gao-Feng], Zhang, Q.[Qian], Xiang, S.M.[Shi-Ming], Huang, C.[Chang], Mu, L.[Lisen], Wang, X.G.[Xing-Gang],
RENAS: Reinforced Evolutionary Neural Architecture Search,
CVPR19(4782-4791).
IEEE DOI 2002
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Classification and Pattern Recognition .


Last update:Oct 19, 2020 at 15:02:28