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Task analysis, Image recognition, Remote sensing,
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Remote sensing, Task analysis, Computer architecture,
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Computer architecture, Optimization,
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2108
Computer architecture, Search problems, Optimization,
Task analysis, Bridges, Binary codes, Estimation,
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2108
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2108
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Computer architecture, Task analysis, Search problems,
Predictive models, Computational modeling, Training,
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2108
Computer architecture, Task analysis, Object detection, Semantics,
Image segmentation, Search problems, Pose estimation,
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2109
Neural architecture search, Weight-sharing,
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WACV21(256-265)
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2106
Training, Measurement, Adaptation models,
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2106
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ICPR21(4789-4796)
IEEE DOI
2105
Microprocessors, Computer architecture, Image classification
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Hirakawa, T.[Tsubasa],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Improving reliability of attention branch network by introducing
uncertainty,
ICPR21(1536-1542)
IEEE DOI
2105
Bayesian neural network to account for uncertainity in the answer.
Uncertainty, Image recognition, Bayes methods,
Reliability, Object recognition, Convolutional neural networks
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Zhang, B.[Bo],
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Li, Q.[Qingyuan],
Fast, Accurate and Lightweight Super-Resolution with Neural
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ICPR21(59-64)
IEEE DOI
2105
Training, Performance evaluation, PSNR, Image coding,
Superresolution, Computer architecture, Reinforcement learning
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Wang, L.[Liuan],
Sun, J.[Jun],
Sun, L.[Li],
Kobashi, H.[Hiromichi],
Imamura, N.[Nobutaka],
NAS-EOD: an end-to-end Neural Architecture Search method for
Efficient Object Detection,
ICPR21(1446-1451)
IEEE DOI
2105
Performance evaluation, Training, Adaptation models,
Image edge detection, Search methods, Pipelines, Graphics processing units
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Akio, F.[Fernando],
Lie, M.[Maiko],
Schwartz, W.R.[William Robson],
Stage-Wise Neural Architecture Search,
ICPR21(1985-1992)
IEEE DOI
2105
Design methodology, Memory management,
Computer architecture, Search problems, Pattern recognition
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Agudo, A.[Antonio],
Moreno-Noguer, F.[Francesc],
E-DNAS: Differentiable Neural Architecture Search for Embedded
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ICPR21(4704-4711)
IEEE DOI
2105
Measurement, Training, Embedded systems, Search methods,
Presence network agents, System-on-chip, Kernel, Deep Learning,
Convolutional Meta Kernels
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Ahn, J.Y.[Joon Young],
Cho, N.I.[Nam Ik],
Neural Architecture Search for Image Super-Resolution Using Densely
Constructed Search Space: DeCoNAS,
ICPR21(4829-4836)
IEEE DOI
2105
Superresolution, Network architecture, Search problems,
Complexity theory, Pattern recognition,
Task analysis
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Peter, D.[David],
Roth, W.[Wolfgang],
Pernkopf, F.[Franz],
Resource-Efficient DNNs for Keyword Spotting using Neural
Architecture Search and Quantization,
ICPR21(9273-9279)
IEEE DOI
2105
Performance evaluation, Quantization (signal), Microcontrollers,
Memory management, Internet, Pattern recognition,
weight quantization
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Kyrkou, C.[Christos],
Theocharides, T.[Theocharis],
Operation and Topology Aware Fast Differentiable Architecture Search,
ICPR21(9666-9673)
IEEE DOI
2105
Convolution, Microprocessors, Architecture, Computer architecture,
Network architecture, Search problems, Topology
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Yous, H.[Hamza],
Sinha, S.[Saksham],
Byrne, J.[Jonathan],
VPU Specific CNNs through Neural Architecture Search,
ICPR21(9772-9779)
IEEE DOI
2105
Performance evaluation, Training, Knowledge engineering,
Neural networks, Graphics processing units, Computer architecture
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Magistrali, G.,
Landro, N.,
Grassa, R.L.,
Improving the Efficient Neural Architecture Search via Rewarding
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IVCNZ20(1-6)
IEEE DOI
2012
Training, Deep learning, Recurrent neural networks,
Computer architecture, Reinforcement learning, Task analysis,
Classification
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Yuan, G.,
Xue, B.,
Zhang, M.,
A Graph-Based Approach to Automatic Convolutional Neural Network
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IVCNZ20(1-6)
IEEE DOI
2012
Neural networks, Computer architecture,
Classification algorithms, Convolutional neural networks,
neural architecture search
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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
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ISPRS20(B2:1217-1224).
DOI Link
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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
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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
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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
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ECCV20(XIII:189-204).
Springer DOI
2011
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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
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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
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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
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ECCV20(XV:465-480).
Springer DOI
2011
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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
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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
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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
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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
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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
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Chen, X.[Xin],
Duan, Y.W.[Ya-Wen],
Chen, Z.W.[Ze-Wei],
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
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ECCV20(XIX:185-202).
Springer DOI
2011
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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
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ECCV20(VII:702-717).
Springer DOI
2011
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Tian, Y.[Yuan],
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Huang, Z.W.[Zhi-Wu],
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Off-policy Reinforcement Learning for Efficient and Effective GAN
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ECCV20(VII:175-192).
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2011
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Tzimiropoulos, G.[Georgios],
Bats: Binary Architecture Search,
ECCV20(XXIII:309-325).
Springer DOI
2011
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Liu, Z.J.[Zhi-Jian],
Zhao, S.Y.[Sheng-Yu],
Lin, Y.J.[Yu-Jun],
Lin, J.[Ji],
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Searching Efficient 3d Architectures with Sparse Point-voxel
Convolution,
ECCV20(XXVIII:685-702).
Springer DOI
2011
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Yuan, Z.H.[Zhi-Hang],
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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
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ECCV20(II:175-192).
Springer DOI
2011
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Liu, C.X.[Chen-Xi],
Dollár, P.[Piotr],
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Girshick, R.[Ross],
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Xie, S.N.[Sai-Ning],
Are Labels Necessary for Neural Architecture Search?,
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Springer DOI
2011
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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
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
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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
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Srinivas, S.[Suraj],
Babu, V.[Venkatesh],
Learning Neural Network Architectures using Backpropagation,
BMVC16(xx-yy).
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Hasegawa, R.[Ryoma],
Hotta, K.[Kazuhiro],
PLSNet: A simple network using Partial Least Squares regression for
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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
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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
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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
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Classification and Pattern Recognition .