14.5.8.3 Neural Architecture, Neural Architecture Search

Chapter Contents (Back)
Neural Networks. Neural Architecture Search. 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

Liu, J.H.[Jia-Heng], Zhou, S.F.[Shun-Feng], Wu, Y.C.[Yi-Chao], Chen, K.[Ken], Ouyang, W.L.[Wan-Li], Xu, D.[Dong],
Block Proposal Neural Architecture Search,
IP(30), 2021, pp. 15-25.
IEEE DOI 2011
Proposals, Computer architecture, Task analysis, DNA, Convolution, Network architecture, Evolutionary computation, image classification BibRef

Jing, W.P.[Wei-Peng], Ren, Q.L.[Quan-Lin], Zhou, J.[Jun], Song, H.B.[Hou-Bing],
AutoRSISC: Automatic design of neural architecture for remote sensing image scene classification,
PRL(140), 2020, pp. 186-192.
Elsevier DOI 2012
Deep learning, High resolution remote sensing, Network architecture search (NAS), Image classification BibRef

Nakai, K.[Kohei], Matsubara, T.[Takashi], Uehara, K.[Kuniaki],
Neural Architecture Search for Convolutional Neural Networks with Attention,
IEICE(E104-D), No. 2, February 2021, pp. 312-321.
WWW Link. 2102
BibRef

Yu, Q.[Qian], Song, J.F.[Ji-Fei], Song, Y.Z.[Yi-Zhe], Chen, H.L.[Han-Lin], Zhuo, L.[Li'an], Zhang, B.C.[Bao-Chang], Zheng, X.W.[Xia-Wu], Liu, J.Z.[Jian-Zhuang], Ji, R.R.[Rong-Rong], Doermann, D.[David], Guo, G.D.[Guo-Dong],
Binarized Neural Architecture Search for Efficient Object Recognition,
IJCV(129), No. 2, February 2021, pp. 501-516.
Springer DOI 2102
BibRef

Wang, J.J.[Jun-Jue], Zhong, Y.F.[Yan-Fei], Zheng, Z.[Zhuo], Ma, A.L.[Ai-Long], Zhan, L.P.[Liang-Pei],
RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks,
GeoRS(59), No. 3, March 2021, pp. 2520-2534.
IEEE DOI 2103
Task analysis, Image recognition, Remote sensing, Computer architecture, Neural networks, Feature extraction, search for convolutional neural networks (CNNs) BibRef

Chen, X.[Xin], Xie, L.X.[Ling-Xi], Wu, J.[Jun], Tian, Q.[Qi],
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild,
IJCV(129), No. 3, March 2021, pp. 638-655.
Springer DOI 2103
Neural Architecture Search. 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

Zhang, Z., Liu, Q.,
Spike-Event-Driven Deep Spiking Neural Network With Temporal Encoding,
SPLetters(28), 2021, pp. 484-488.
IEEE DOI 2103
Neurons, Encoding, Feature extraction, Computational modeling, Task analysis, Image coding, Biological neural networks, spiking neural network BibRef

Liu, X.B.[Xiao-Bo], Zhang, C.C.[Chao-Chao], Cai, Z.H.[Zhi-Hua], Yang, J.F.[Jian-Feng], Zhou, Z.L.[Zhi-Lang], Gong, X.[Xin],
Continuous Particle Swarm Optimization-Based Deep Learning Architecture Search for Hyperspectral Image Classification,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Liu, H.Y.[Hong-Ying], Xu, D.[Derong], Zhu, T.[Tianwen], Shang, F.H.[Fan-Hua], Liu, Y.Y.[Yuan-Yuan], Lu, J.H.[Jian-Hua], Yang, R.[Ri],
Graph Convolutional Networks by Architecture Search for PolSAR Image Classification,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef


Gallo, I., Magistrali, G., Landro, N., Grassa, R.L.,
Improving the Efficient Neural Architecture Search via Rewarding Modifications,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Deep learning, Recurrent neural networks, Computer architecture, Reinforcement learning, Task analysis, Classification BibRef

Yuan, G., Xue, B., Zhang, M.,
A Graph-Based Approach to Automatic Convolutional Neural Network Construction for Image Classification,
IVCNZ20(1-6)
IEEE DOI 2012
Neural networks, Computer architecture, Classification algorithms, Convolutional neural networks, neural architecture search BibRef

Li, J.H.[Ji-Hao], Diao, W.H.[Wen-Hui], Sun, X.[Xian], Feng, Y.C.[Ying-Chao], Zhang, W.K.[Wen-Kai], Chang, Z.H.[Zhong-Han], Fu, K.[Kun],
Automated and Lightweight Network Design Via Random Search for Remote Sensing Image Scene Classification,
ISPRS20(B2:1217-1224).
DOI Link 2012
BibRef

Chen, Y.C.[Yun-Chun], Gao, C.[Chen], Robb, E.[Esther], Huang, J.B.[Jia-Bin],
NAS-DIP: Learning Deep Image Prior with Neural Architecture Search,
ECCV20(XVIII:442-459).
Springer DOI 2012
BibRef

Chen, H.L.[Han-Lin], Zhang, B.C.[Bao-Chang], Xue, S.[Song], Gong, X.[Xuan], Liu, H.[Hong], Ji, R.R.[Rong-Rong], Doermann, D.[David],
Anti-Bandit Neural Architecture Search for Model Defense,
ECCV20(XIII:70-85).
Springer DOI 2011
BibRef

Ning, X.F.[Xue-Fei], Zheng, Y.[Yin], Zhao, T.C.[Tian-Chen], Wang, Y.[Yu], Yang, H.Z.[Hua-Zhong],
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS,
ECCV20(XIII:189-204).
Springer DOI 2011
BibRef

Hu, Y.[Yibo], Wu, X.[Xiang], He, R.[Ran],
TF-NAS: Rethinking Three Search Freedoms of Latency-constrained Differentiable Neural Architecture Search,
ECCV20(XV:123-139).
Springer DOI 2011
BibRef

Xu, H.[Hang], Wang, S.[Shaoju], Cai, X.Y.[Xin-Yue], Zhang, W.[Wei], Liang, X.D.[Xiao-Dan], Li, Z.G.[Zhen-Guo],
Curvelane-NAS: Unifying Lane-sensitive Architecture Search and Adaptive Point Blending,
ECCV20(XV:689-704).
Springer DOI 2011
BibRef

Chu, X.X.[Xiang-Xiang], Zhou, T.B.[Tian-Bao], Zhang, B.[Bo], Li, J.X.[Ji-Xiang],
Fair Darts: Eliminating Unfair Advantages in Differentiable Architecture Search,
ECCV20(XV:465-480).
Springer DOI 2011
BibRef

Dai, X.Y.[Xi-Yang], Chen, D.D.[Dong-Dong], Liu, M.C.[Meng-Chen], Chen, Y.P.[Yin-Peng], Yuan, L.[Lu],
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search,
ECCV20(XXVII:584-600).
Springer DOI 2011
BibRef

Hu, Y.[Yutao], Jiang, X.L.[Xiao-Long], Liu, X.H.[Xu-Hui], Zhang, B.C.[Bao-Chang], Han, J.G.[Jun-Gong], Cao, X.B.[Xian-Bin], Doermann, D.[David],
NAS-Count: Counting-by-density with Neural Architecture Search,
ECCV20(XXII:747-766).
Springer DOI 2011
BibRef

Yuan, Q.W.[Qiong-Wen], He, J.W.[Jing-Wei], Yu, L.[Lei], Zheng, G.[Gang],
AIM-Net: Bring Implicit Euler to Network Design,
ICIP20(1926-1930)
IEEE DOI 2011
Neural networks, Adaptation models, Convergence, Mathematical model, Image resolution, Signal resolution, image superresolution BibRef

Howard-Jenkins, H.[Henry], Li, Y.[Yiwen], Prisacariu, V.A.[Victor Adrian],
Gross: Group-size Series Decomposition for Grouped Architecture Search,
ECCV20(XXVI:18-33).
Springer DOI 2011
BibRef

Hu, Y.M.[Yi-Ming], Liang, Y.[Yuding], Guo, Z.[Zichao], Wan, R.[Ruosi], Zhang, X.Y.[Xiang-Yu], Wei, Y.[Yichen], Gu, Q.Y.[Qing-Yi], Sun, J.[Jian],
Angle-based Search Space Shrinking for Neural Architecture Search,
ECCV20(XIX:119-134).
Springer DOI 2011
BibRef

Chen, X.[Xin], Duan, Y.[Yawen], Chen, Z.[Zewei], Xu, H.[Hang], Chen, Z.[Zihao], Liang, X.D.[Xiao-Dan], Zhang, T.[Tong], Li, Z.G.[Zhen-Guo],
Catch: Context-based Meta Reinforcement Learning for Transferrable Architecture Search,
ECCV20(XIX:185-202).
Springer DOI 2011
BibRef

Yu, J.H.[Jia-Hui], Jin, P.C.[Peng-Chong], Liu, H.X.[Han-Xiao], Bender, G.[Gabriel], Kindermans, P.J.[Pieter-Jan], Tan, M.X.[Ming-Xing], Huang, T.[Thomas], Song, X.D.[Xiao-Dan], Pang, R.M.[Ruo-Ming], Le, Q.[Quoc],
Bignas: Scaling up Neural Architecture Search with Big Single-stage Models,
ECCV20(VII:702-717).
Springer DOI 2011
BibRef

Tian, Y.[Yuan], Wang, Q.[Qin], Huang, Z.W.[Zhi-Wu], Li, W.[Wen], Dai, D.X.[Deng-Xin], Yang, M.H.[Ming-Hao], Wang, J.[Jun], Fink, O.[Olga],
Off-policy Reinforcement Learning for Efficient and Effective GAN Architecture Search,
ECCV20(VII:175-192).
Springer DOI 2011
BibRef

Lu, Z.C.[Zhi-Chao], Deb, K.[Kalyanmoy], Goodman, E.[Erik], Banzhaf, W.[Wolfgang], Boddeti, V.N.[Vishnu Naresh],
Nsganetv2: Evolutionary Multi-objective Surrogate-assisted Neural Architecture Search,
ECCV20(I:35-51).
Springer DOI 2011
BibRef

Bulat, A.[Adrian], Martinez, B.[Brais], Tzimiropoulos, G.[Georgios],
Bats: Binary Architecture Search,
ECCV20(XXIII:309-325).
Springer DOI 2011
BibRef

Tang, H.T.[Hao-Tian], Liu, Z.J.[Zhi-Jian], Zhao, S.Y.[Sheng-Yu], Lin, Y.J.[Yu-Jun], Lin, J.[Ji], Wang, H.R.[Han-Rui], Han, S.[Song],
Searching Efficient 3d Architectures with Sparse Point-voxel Convolution,
ECCV20(XXVIII:685-702).
Springer DOI 2011
BibRef

Yuan, Z.H.[Zhi-Hang], Wu, B.Z.[Bing-Zhe], Sun, G.Y.[Guang-Yu], Liang, Z.[Zheng], Zhao, S.W.[Shi-Wan], Bi, W.C.[Wei-Chen],
S2dnas: Transforming Static Cnn Model for Dynamic Inference via Neural Architecture Search,
ECCV20(II:175-192).
Springer DOI 2011
BibRef

Liu, C.X.[Chen-Xi], Dollár, P.[Piotr], He, K.M.[Kai-Ming], Girshick, R.[Ross], Yuille, A.L.[Alan L.], Xie, S.N.[Sai-Ning],
Are Labels Necessary for Neural Architecture Search?,
ECCV20(IV:798-813).
Springer DOI 2011
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

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.K.[Shou-Kang], Xie, S.R.[Si-Rui], Zheng, H.H.[He-Hui], Liu, C.X.[Chun-Xiao], Shi, J.P.[Jian-Ping], Liu, X.Y.[Xun-Ying], Lin, D.H.[Da-Hua],
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

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

Chen, X.[Xin], Xie, L.X.[Ling-Xi], Wu, J.[Jun], Tian, Q.[Qi],
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation,
ICCV19(1294-1303)
IEEE DOI 2004
Code, Search.
WWW Link. approximation theory, image recognition, learning (artificial intelligence), neural net architecture, Computational modeling BibRef

Banerjee, S., Chakraborty, S.,
Deepsub: A Novel Subset Selection Framework for Training Deep Learning Architectures,
ICIP19(1615-1619)
IEEE DOI 1910
Submodular optimization, Deep learning BibRef

Xiong, Y., Mehta, R., Singh, V.,
Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?,
ICCV19(1901-1910)
IEEE DOI 2004
learning (artificial intelligence), neural nets, optimisation, neural network architecture search, empirical feedback, Heuristic algorithms BibRef

Zhao, R., Luk, W.,
Efficient Structured Pruning and Architecture Searching for Group Convolution,
NeruArch19(1961-1970)
IEEE DOI 2004
convolutional neural nets, group theory, network theory (graphs), neural net architecture, search problems, network pruning, efficient inference BibRef

Li, X.[Xin], Zhou, Y.M.[Yi-Ming], Pan, Z.[Zheng], Feng, J.[Jiashi],
Partial Order Pruning: For Best Speed/Accuracy Trade-Off in Neural Architecture Search,
CVPR19(9137-9145).
IEEE DOI 2002
BibRef

Guo, M.H.[Ming-Hao], Zhong, Z.[Zhao], Wu, W.[Wei], Lin, D.[Dahua], Yan, J.J.[Jun-Jie],
IRLAS: Inverse Reinforcement Learning for Architecture Search,
CVPR19(9013-9021).
IEEE DOI 2002
search network structures that are topologically inspired by human-designed network 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.H.[Yun-He], 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:May 10, 2021 at 18:51:10