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Deep learning architectures
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IEEE DOI
1909
Feature extraction, Deep learning, Hyperspectral imaging,
Convolution, Training, Convolutional neural network (CNN),
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1909
Reinforcement learning, Convolutional neural networks,
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2008
Computer architecture, Tuning, Genetic algorithms,
Evolutionary computation, Manuals, Genetics, Evolution (biology),
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Dong, H.,
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Automatic Design of CNNs via Differentiable Neural Architecture
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IEEE DOI
2008
Computer architecture, Personal digital assistants,
Deep learning, Search problems, Neural networks,
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Liu, J.H.[Jia-Heng],
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Wu, Y.C.[Yi-Chao],
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Block Proposal Neural Architecture Search,
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IEEE DOI
2011
Proposals, Computer architecture, Task analysis, DNA, Convolution,
Network architecture, Evolutionary computation,
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Jing, W.P.[Wei-Peng],
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AutoRSISC: Automatic design of neural architecture for remote sensing
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Elsevier DOI
2012
Deep learning, High resolution remote sensing,
Network architecture search (NAS), Image classification
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Nakai, K.[Kohei],
Matsubara, T.[Takashi],
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IEICE(E104-D), No. 2, February 2021, pp. 312-321.
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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,
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Springer DOI
2102
BibRef
Zhao, J.[Junhe],
Xu, S.[Sheng],
Zhang, B.C.[Bao-Chang],
Gu, J.X.[Jia-Xin],
Doermann, D.[David],
Guo, G.D.[Guo-Dong],
Towards Compact 1-bit CNNs via Bayesian Learning,
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Springer DOI
2202
BibRef
Wang, J.J.[Jun-Jue],
Zhong, Y.F.[Yan-Fei],
Zheng, Z.[Zhuo],
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RSNet: The Search for Remote Sensing Deep Neural Networks in
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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],
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Springer DOI
2103
Neural Architecture Search.
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Chen, Z.S.[Zheng-Su],
Xie, L.X.[Ling-Xi],
Niu, J.W.[Jian-Wei],
Liu, X.F.[Xue-Feng],
Wei, L.H.[Long-Hui],
Tian, Q.[Qi],
Network Adjustment: Channel and Block Search Guided by Resource
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IJCV(130), No. 3, March 2022, pp. 820-835.
Springer DOI
2203
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio,
CVPR20(10655-10664)
IEEE DOI
2008
Training, Computer architecture, Neural networks,
Channel estimation, Pipelines, Standards
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
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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
Chen, Z.Q.[Zhi-Qiang],
Xu, T.B.[Ting-Bing],
Liao, W.J.[Wei-Jian],
Li, Z.C.[Zheng-Cheng],
Li, J.P.[Jin-Peng],
Liu, C.L.[Cheng-Lin],
He, H.G.[Hui-Guang],
SNAP: Shaping neural architectures progressively via information
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PR(116), 2021, pp. 107923.
Elsevier DOI
2106
Auto-generated neural architectures, Information density,
Greedy strategy, Progressively, Efficient and adaptive
BibRef
Khatib, R.[Rajaei],
Simon, D.[Dror],
Elad, M.[Michael],
Learned Greedy Method (LGM):
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JVCIR(77), 2021, pp. 103095.
Elsevier DOI
2106
Sparse representation, Orthogonal Matching Pursuit,
Unfolding pursuit algorithms, Deraining
BibRef
Peng, C.[Cheng],
Li, Y.Y.[Yang-Yang],
Jiao, L.C.[Li-Cheng],
Shang, R.H.[Rong-Hua],
Efficient Convolutional Neural Architecture Search for Remote Sensing
Image Scene Classification,
GeoRS(59), No. 7, July 2021, pp. 6092-6105.
IEEE DOI
2106
Remote sensing, Task analysis, Computer architecture,
Feature extraction, Data models, Machine learning, Semantics,
scene classification
BibRef
Zhang, B.F.[Bao Feng],
Zhou, G.Q.[Guo Qiang],
Control the number of skip-connects to improve robustness of the NAS
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IET-CV(15), No. 5, 2021, pp. 356-365.
DOI Link
2107
neural architecture search
BibRef
Zhang, X.B.[Xin-Bang],
Huang, Z.[Zehao],
Wang, N.Y.[Nai-Yan],
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Pan, C.H.[Chun-Hong],
You Only Search Once: Single Shot Neural Architecture Search via
Direct Sparse Optimization,
PAMI(43), No. 9, September 2021, pp. 2891-2904.
IEEE DOI
2108
Computer architecture, Optimization,
Learning (artificial intelligence), Task analysis, Acceleration,
sparse optimization
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Zhang, X.B.[Xin-Bang],
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Meng, G.F.[Gao-Feng],
Xiang, S.M.[Shi-Ming],
Lin, Z.C.[Zhou-Chen],
Pan, C.H.[Chun-Hong],
DATA: Differentiable ArchiTecture Approximation With Distribution
Guided Sampling,
PAMI(43), No. 9, September 2021, pp. 2905-2920.
IEEE DOI
2108
Computer architecture, Search problems, Optimization,
Task analysis, Bridges, Binary codes, Estimation,
distribution guided sampling
BibRef
Zheng, X.[Xiawu],
Ji, R.R.[Rong-Rong],
Chen, Y.H.[Yu-Hang],
Wang, Q.[Qiang],
Zhang, B.C.[Bao-Chang],
Chen, J.[Jie],
Ye, Q.X.[Qi-Xiang],
Huang, F.Y.[Fei-Yue],
Tian, Y.H.[Yong-Hong],
MIGO-NAS: Towards Fast and Generalizable Neural Architecture Search,
PAMI(43), No. 9, September 2021, pp. 2936-2952.
IEEE DOI
2108
Computer architecture, Training, Dynamic programming,
Graphics processing units, Task analysis,
dynamic programming
BibRef
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Xie, L.X.[Ling-Xi],
Dai, W.R.[Wen-Rui],
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Qi, G.J.[Guo-Jun],
Xiong, H.K.[Hong-Kai],
Tian, Q.[Qi],
Partially-Connected Neural Architecture Search for Reduced
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PAMI(43), No. 9, September 2021, pp. 2953-2970.
IEEE DOI
2108
Computer architecture, Redundancy, Network architecture,
Stability analysis, Microprocessors, Space exploration,
normalization
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
Lu, Z.C.[Zhi-Chao],
Sreekumar, G.[Gautam],
Goodman, E.[Erik],
Banzhaf, W.[Wolfgang],
Deb, K.[Kalyanmoy],
Boddeti, V.N.[Vishnu Naresh],
Neural Architecture Transfer,
PAMI(43), No. 9, September 2021, pp. 2971-2989.
IEEE DOI
2108
BibRef
Earlier: A1, A5, A3, A4, A6, Only:
Nsganetv2: Evolutionary Multi-objective Surrogate-assisted Neural
Architecture Search,
ECCV20(I:35-51).
Springer DOI
2011
Computer architecture, Task analysis, Search problems,
Predictive models, Computational modeling, Training,
evolutionary algorithms
BibRef
Fang, J.M.[Jie-Min],
Sun, Y.Z.[Yu-Zhu],
Zhang, Q.[Qian],
Peng, K.J.[Kang-Jian],
Li, Y.[Yuan],
Liu, W.Y.[Wen-Yu],
Wang, X.G.[Xing-Gang],
FNA++: Fast Network Adaptation via Parameter Remapping and
Architecture Search,
PAMI(43), No. 9, September 2021, pp. 2990-3004.
IEEE DOI
2108
Computer architecture, Task analysis, Object detection, Semantics,
Image segmentation, Search problems, Pose estimation,
neural architecture search
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
Tian, Y.J.[Yun-Jie],
Liu, C.[Chang],
Xie, L.X.[Ling-Xi],
jiao, J.B.[Jian-Bin],
Ye, Q.[Qixiang],
Discretization-aware architecture search,
PR(120), 2021, pp. 108186.
Elsevier DOI
2109
Neural architecture search, Weight-sharing,
Discretization-aware, Imbalanced network configuration
BibRef
Lukasik, J.[Jovita],
Friede, D.[David],
Stuckenschmidt, H.[Heiner],
Keuper, M.[Margret],
Neural Architecture Performance Prediction Using Graph Neural Networks,
GCPR20(188-201).
Springer DOI
2110
BibRef
Wang, N.[Ning],
Gao, Y.[Yang],
Chen, H.[Hao],
Wang, P.[Peng],
Tian, Z.[Zhi],
Shen, C.H.[Chun-Hua],
Zhang, Y.N.[Yan-Ning],
NAS-FCOS: Efficient Search for Object Detection Architectures,
IJCV(129), No. 12, December 2021, pp. 3299-3312.
Springer DOI
2111
BibRef
Earlier:
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
Wang, Z.Y.[Zu-Yuan],
Remote Sensing Scene Classification via Multi-Branch Local Attention
Network,
IP(31), 2022, pp. 99-109.
IEEE DOI
2112
Different classes aren't that different.
Remote sensing, Feature extraction,
Convolutional neural networks, Image color analysis,
attention mechanism
BibRef
Chen, J.[Jie],
Huang, H.Z.[Hao-Zhe],
Peng, J.[Jian],
Zhu, J.W.[Jia-Wei],
Chen, L.[Li],
Tao, C.[Chao],
Li, H.F.[Hai-Feng],
Contextual Information-Preserved Architecture Learning for
Remote-Sensing Scene Classification,
GeoRS(60), 2022, pp. 1-14.
IEEE DOI
2112
Remote sensing, Task analysis, Convolution,
Architecture, Semantics, Space exploration, scene classification
BibRef
Zhang, Z.[Zhen],
Liu, S.[Shanghao],
Zhang, Y.[Yang],
Chen, W.[Wenbo],
RS-DARTS: A Convolutional Neural Architecture Search for Remote
Sensing Image Scene Classification,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Li, G.[Gen],
Gu, Y.T.[Yuan-Tao],
Ding, J.[Jie],
L_1 Regularization in Two-Layer Neural Networks,
SPLetters(29), 2022, pp. 135-139.
IEEE DOI
2202
Biological neural networks, Complexity theory, Neurons, Training,
Approximation error, Convergence, Computational modeling, regularization
BibRef
Shi, Z.L.[Zeng-Lin],
Mettes, P.S.[Pascal S.],
Maji, S.[Subhransu],
Snoek, C.G.M.[Cees G. M.],
On Measuring and Controlling the Spectral Bias of the Deep Image Prior,
IJCV(130), No. 1, January 2022, pp. 885-908.
Springer DOI
2204
Code, Spectal Bias.
WWW Link.
BibRef
Guo, Q.[Qingbei],
Wu, X.J.[Xiao-Jun],
Kittler, J.V.[Josef V.],
Feng, Z.Q.[Zhi-Quan],
Differentiable neural architecture learning for efficient neural
networks,
PR(126), 2022, pp. 108448.
Elsevier DOI
2204
Deep neural network, Convolutional neural network,
Neural architecture search, Automated machine learning
BibRef
Dong, N.Q.[Nan-Qing],
Kampffmeyer, M.[Michael],
Voiculescu, I.[Irina],
Xing, E.[Eric],
Negational symmetry of quantum neural networks for binary pattern
classification,
PR(129), 2022, pp. 108750.
Elsevier DOI
2206
Deep learning, Quantum machine learning,
Binary pattern classification, Representation learning, Symmetry
BibRef
Dong, X.[Xuanyi],
Liu, L.[Lu],
Musial, K.[Katarzyna],
Gabrys, B.[Bogdan],
NATS-Bench:
Benchmarking NAS Algorithms for Architecture Topology and Size,
PAMI(44), No. 7, July 2022, pp. 3634-3646.
IEEE DOI
2206
Computer architecture, Topology, Microprocessors,
Benchmark testing, Training, Search problems, Deep learning,
deep learning
BibRef
Wang, R.C.[Ruo-Chen],
Chen, X.N.[Xiang-Ning],
Cheng, M.[Minhao],
Tang, X.C.[Xiao-Cheng],
Hsieh, C.J.[Cho-Jui],
RANK-NOSH: Efficient Predictor-Based Architecture Search via
Non-Uniform Successive Halving,
ICCV21(10357-10366)
IEEE DOI
2203
Training, Costs, Scheduling algorithms, Computer architecture,
Prediction algorithms, Computational efficiency,
Representation learning
BibRef
Ci, Y.Z.[Yuan-Zheng],
Lin, C.[Chen],
Sun, M.[Ming],
Chen, B.[Boyu],
Zhang, H.[Hongwen],
Ouyang, W.L.[Wan-Li],
Evolving Search Space for Neural Architecture Search,
ICCV21(6639-6649)
IEEE DOI
2203
Codes, Automation, Extraterrestrial phenomena,
Computer architecture, Performance gain, Search problems,
BibRef
Chen, Y.[Ying],
Mao, F.[Feng],
Song, J.[Jie],
Wang, X.C.[Xin-Chao],
Wang, H.Q.[Hui-Qiong],
Song, M.L.[Ming-Li],
Self-born Wiring for Neural Trees,
ICCV21(5027-5036)
IEEE DOI
2203
Wiring, Deep learning, Representation learning, Greedy algorithms,
Scalability, Neural networks, Explainable AI
BibRef
Xue, S.[Song],
Wang, R.[Runqi],
Zhang, B.C.[Bao-Chang],
Wang, T.[Tian],
Guo, G.D.[Guo-Dong],
Doermann, D.[David],
IDARTS: Interactive Differentiable Architecture Search,
ICCV21(1143-1152)
IEEE DOI
2203
Training, Couplings, Backpropagation, Backtracking, Costs,
Computer architecture, Recognition and classification,
Optimization and learning methods
BibRef
Deng, C.[Congyue],
Litany, O.[Or],
Duan, Y.[Yueqi],
Poulenard, A.[Adrien],
Tagliasacchi, A.[Andrea],
Guibas, L.J.[Leonidas J.],
Vector Neurons: A General Framework for SO(3)-Equivariant Networks,
ICCV21(12180-12189)
IEEE DOI
2203
Geometry, Shape, Neurons, Computer architecture,
Network architecture, Task analysis, 3D from multiview and other sensors
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Xu, R.[Ruijun],
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural
Architecture Search,
ICCV21(12219-12228)
IEEE DOI
2203
Training, Computational modeling, Pipelines,
Graphics processing units, Computer architecture,
Recognition and classification
BibRef
Moons, B.[Bert],
Noorzad, P.[Parham],
Skliar, A.[Andrii],
Mariani, G.[Giovanni],
Mehta, D.[Dushyant],
Lott, C.[Chris],
Blankevoort, T.[Tijmen],
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces,
ICCV21(12209-12218)
IEEE DOI
2203
Knowledge engineering, Image coding, Pipelines, Neural networks,
Graphics processing units, Computer architecture,
grouping and shape
BibRef
Liu, Y.[Yuqiao],
Tang, Y.[Yehui],
Sun, Y.[Yanan],
Homogeneous Architecture Augmentation for Neural Predictor,
ICCV21(12229-12238)
IEEE DOI
2203
Training, Performance evaluation, Deep learning, Neural networks,
Training data, Computer architecture, Transforms,
BibRef
Wang, Y.[Yaoming],
Liu, Y.C.[Yu-Chen],
Dai, W.[Wenrui],
Li, C.[Chenglin],
Zou, J.[Junni],
Xiong, H.K.[Hong-Kai],
Learning Latent Architectural Distribution in Differentiable Neural
Architecture Search via Variational Information Maximization,
ICCV21(12292-12301)
IEEE DOI
2203
Error analysis, Computer architecture, Search problems,
Data models, Convolutional neural networks, Mutual information,
BibRef
Mok, J.[Jisoo],
Na, B.G.[Byung-Gook],
Choe, H.[Hyeokjun],
Yoon, S.[Sungroh],
AdvRush: Searching for Adversarially Robust Neural Architectures,
ICCV21(12302-12312)
IEEE DOI
2203
Training, Deep learning, Neural networks, Computer architecture,
Benchmark testing, Linear programming,
BibRef
Peng, J.F.[Jie-Feng],
Zhang, J.Q.[Ji-Qi],
Li, C.L.[Chang-Lin],
Wang, G.R.[Guang-Run],
Liang, X.D.[Xiao-Dan],
Lin, L.[Liang],
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet
Training Consistency Shift,
ICCV21(12334-12344)
IEEE DOI
2203
Training, Correlation, Computer architecture, Search problems,
Task analysis, Machine learning architectures and formulations,
Recognition and classification
BibRef
Chen, B.[Boyu],
Li, P.X.[Pei-Xia],
Li, C.[Chuming],
Li, B.[Baopu],
Bai, L.[Lei],
Lin, C.[Chen],
Sun, M.[Ming],
Yan, J.J.[Jun-Jie],
Ouyang, W.L.[Wan-Li],
GLiT: Neural Architecture Search for Global and Local Image
Transformer,
ICCV21(12-21)
IEEE DOI
2203
Visualization, Image recognition, Correlation,
Computer architecture, Evolutionary computation, Transformers,
BibRef
Chen, B.[Boyu],
Li, P.X.[Pei-Xia],
Li, B.[Baopu],
Lin, C.[Chen],
Li, C.[Chuming],
Sun, M.[Ming],
Yan, J.J.[Jun-Jie],
Ouyang, W.L.[Wan-Li],
BN-NAS: Neural Architecture Search with Batch Normalization,
ICCV21(307-316)
IEEE DOI
2203
Training, Codes, Computer architecture, Network architecture,
Convergence, Recognition and classification,
BibRef
Yuan, K.[Kun],
Li, Q.Q.[Quan-Quan],
Guo, S.P.[Shao-Peng],
Chen, D.P.[Da-Peng],
Zhou, A.[Aojun],
Yu, F.W.[Feng-Wei],
Liu, Z.[Ziwei],
Differentiable Dynamic Wirings for Neural Networks,
ICCV21(317-326)
IEEE DOI
2203
Wiring, Training, Costs, Computational modeling, Aggregates,
Neural networks, Computer architecture,
Efficient training and inference methods
BibRef
Zhou, D.Q.[Da-Quan],
Jin, X.J.[Xiao-Jie],
Lian, X.C.[Xiao-Chen],
Yang, L.J.[Lin-Jie],
Xue, Y.J.[Yu-Jing],
Hou, Q.B.[Qi-Bin],
Feng, J.S.[Jia-Shi],
AutoSpace: Neural Architecture Search with Less Human Interference,
ICCV21(327-336)
IEEE DOI
2203
Knowledge engineering, Costs, Computational modeling,
Computer architecture, Interference, Manuals,
Machine learning architectures and formulations
BibRef
Hu, J.[Jie],
Cao, L.J.[Liu-Juan],
Tong, T.[Tong],
Ye, Q.X.[Qi-Xiang],
Zhang, S.C.[Sheng-Chuan],
Li, K.[Ke],
Huang, F.Y.[Fei-Yue],
Shao, L.[Ling],
Ji, R.R.[Rong-Rong],
Architecture Disentanglement for Deep Neural Networks,
ICCV21(652-661)
IEEE DOI
2203
Deep learning, Codes, Semantics, Neural networks,
Computer architecture, Network architecture, Explainable AI,
Representation learning
BibRef
Lin, M.[Ming],
Wang, P.[Pichao],
Sun, Z.H.[Zhen-Hong],
Chen, H.[Hesen],
Sun, X.[Xiuyu],
Qian, Q.[Qi],
Li, H.[Hao],
Jin, R.[Rong],
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition,
ICCV21(337-346)
IEEE DOI
2203
Training, Image recognition, Architecture, Computational modeling,
Graphics processing units, Computer architecture,
Machine learning architectures and formulations
BibRef
Jeevan, P.[Pranav],
Sethi, A.[Amit],
Resource-efficient Hybrid X-formers for Vision,
WACV22(3555-3563)
IEEE DOI
2202
Computational modeling, Memory management,
Graphics processing units, Training data, Transformers, Scene Understanding
BibRef
Simon, C.[Christian],
Koniusz, P.[Piotr],
Petersson, L.[Lars],
Han, Y.[Yan],
Harandi, M.[Mehrtash],
Towards a Robust Differentiable Architecture Search under Label Noise,
WACV22(3584-3594)
IEEE DOI
2202
Convolution, Neural networks, Focusing,
Computer architecture, Manuals, Games, Statistical Methods,
Learning and Optimization Deep Learning
BibRef
Yu, T.[Tan],
Li, X.[Xu],
Cai, Y.F.[Yun-Feng],
Sun, M.M.[Ming-Ming],
Li, P.[Ping],
S2-MLP: Spatial-Shift MLP Architecture for Vision,
WACV22(3615-3624)
IEEE DOI
2202
Training, Visualization, Image recognition,
Convolution, Computer architecture, Transformers,
Deep Learning vision architecture
BibRef
Gong, X.Y.[Xin-Yu],
Chen, W.Y.[Wu-Yang],
Chen, T.L.[Tian-Long],
Wang, Z.Y.[Zhang-Yang],
Sandwich Batch Normalization:
A Drop-In Replacement for Feature Distribution Heterogeneity,
WACV22(2957-2967)
IEEE DOI
2202
Code, Normalization.
WWW Link. Build on DARTS.
Training, Codes, Image synthesis, Computer architecture,
Semisupervised learning, Data models, Robustness, Deep Learning
BibRef
Wan, X.[Xingchen],
Ru, B.X.[Bin-Xin],
Esparança, P.M.[Pedro M.],
Carlucci, F.M.[Fabio M.],
Approximate Neural Architecture Search via Operation Distribution
Learning,
WACV22(3545-3554)
IEEE DOI
2202
Costs, Microprocessors, Computer architecture,
Search problems, Encoding, Robustness,
Deep Learning neural network architectures
BibRef
Xia, X.[Xin],
Xiao, X.F.[Xue-Feng],
Wang, X.[Xing],
Zheng, M.[Min],
Progressive Automatic Design of Search Space for One-Shot Neural
Architecture Search,
WACV22(3525-3534)
IEEE DOI
2202
Couplings, Costs, Neural networks,
Computer architecture, Search problems, Hardware,
Deep Learning Deep Learning -> Efficient Training and
Inference Methods for Networks
BibRef
Henmi, T.[Takahiko],
Zara, E.R.R.[Esmeraldo Ronnie Rey],
Hirohashi, Y.[Yoshihiro],
Kato, T.[Tsuyoshi],
Adaptive Signal Variances:
CNN Initialization Through Modern Architectures,
ICIP21(374-378)
IEEE DOI
2201
Adaptation models, Convolution, Image processing,
Stability analysis, Convolutional neural networks, Standards, Initialization
BibRef
Gupta, S.[Shashank],
Robles-Kelly, A.[Antonio],
Feature-Extracting Functions for Neural Logic Rule Learning,
DICTA20(1-2)
IEEE DOI
2201
Domain knowledge into behavior of neural network.
Training, Knowledge engineering, Sentiment analysis,
Neural networks, Programming, Feature extraction, rule learning
BibRef
Imamura, A.[Akihiro],
Arizumi, N.[Nana],
Gabor Filter Incorporated CNN for Compression,
IVCNZ21(1-5)
IEEE DOI
2201
Gabor in early layers of CNN.
Image coding, Dictionaries, Costs, Convolution, Real-time systems,
Gabor filters, compression, CNN, Gabor
BibRef
Chitty-Venkata, K.T.[Krishna Teja],
Somani, A.K.[Arun K.],
Kothandaraman, S.[Sreenivas],
Searching Architecture and Precision for U-net based Image
Restoration Tasks,
ICIP21(1989-1993)
IEEE DOI
2201
Deep learning, Measurement, Quantization (signal), Tensors,
Computational modeling, Microprocessors, Computer architecture,
Mixed Precision
BibRef
Gaudet, C.J.[Chase J.],
Maida, A.S.[Anthony S.],
Removing Dimensional Restrictions on Complex/Hyper-Complex Neural
Networks,
ICIP21(319-323)
IEEE DOI
2201
Image color analysis, Algebra, Quaternions, Neural networks, MIMICs,
Task analysis, cnn, quaternion, complex, multidimensional
BibRef
Chen, Z.D.[Zhen-Duo],
Liu, F.[Feng],
Zhao, Z.L.[Zheng-Lai],
Let Them Choose What They Want: A Multi-Task CNN Architecture
Leveraging Mid-Level Deep Representations for Face Attribute
Classification,
ICIP21(879-883)
IEEE DOI
2201
Correlation, Image processing, Computer architecture,
Task analysis, Faces, deep learning, multi-task learning, attention
BibRef
Lin, J.L.[Jun-Liang],
Sung, Y.L.[Yi-Lin],
Hong, C.Y.[Cheng-Yao],
Lee, H.H.[Han-Hung],
Liu, T.L.[Tyng-Luh],
The Maximum a Posterior Estimation of Darts,
ICIP21(419-423)
IEEE DOI
2201
Couplings, Image processing, Estimation, Network architecture,
Benchmark testing, Search problems, Neural Architecture Search,
Deep Learning
BibRef
Jiang, B.[Borui],
Mu, Y.D.[Ya-Dong],
Russian Doll Network: Learning Nested Networks for Sample-Adaptive
Dynamic Inference,
NeruArch21(336-344)
IEEE DOI
2112
Bridges, Computational modeling,
Transforms, Computer architecture, Optimization
BibRef
Shen, B.[Biluo],
Xiao, A.[Anqi],
Tian, J.[Jie],
Hu, Z.H.[Zhen-Hua],
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural
Network,
NeruArch21(365-372)
IEEE DOI
2112
Training, Image segmentation, Semantics,
Transfer learning, Object detection, Computer architecture
BibRef
Liu, C.H.[Chia-Hsiang],
Han, Y.S.[Yu-Shin],
Sung, Y.Y.[Yuan-Yao],
Lee, Y.[Yi],
Chiang, H.Y.[Hung-Yueh],
Wu, K.C.A.[Kai-Chi-Ang],
FOX-NAS: Fast, On-device and Explainable Neural Architecture Search,
LPCV21(789-797)
IEEE DOI
2112
Costs, Search methods, Neural networks,
Graphics processing units, Computer architecture
BibRef
Chatzianastasis, M.[Michail],
Dasoulas, G.[George],
Siolas, G.[Georgios],
Vazirgiannis, M.[Michalis],
Graph-based Neural Architecture Search with Operation Embeddings,
NeruArch21(393-402)
IEEE DOI
2112
Training, Correlation, Pipelines,
Computer architecture, Network architecture
BibRef
Hou, P.F.[Peng-Fei],
Jin, Y.[Ying],
Chen, Y.[Yukang],
Single-DARTS: Towards Stable Architecture Search,
NeruArch21(373-382)
IEEE DOI
2112
Systematics, Costs, Codes,
Computer architecture, Stability analysis
BibRef
Devaguptapu, C.[Chaitanya],
Agarwal, D.[Devansh],
Mittal, G.[Gaurav],
Gopalani, P.[Pulkit],
Balasubramanian, V.N.[Vineeth N],
On Adversarial Robustness: A Neural Architecture Search perspective,
AROW21(152-161)
IEEE DOI
2112
Training, Measurement, Deep learning, Analytical models,
Network topology, Neural networks, Computer architecture
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Li, Q.Y.[Qing-Yuan],
Xu, R.J.[Rui-Jun],
Li, X.D.[Xu-Dong],
SCARLET-NAS: Bridging the Gap between Stability and Scalability in
Weight-sharing Neural Architecture Search,
NeruArch21(317-325)
IEEE DOI
2112
Training, Scalability, Perturbation methods,
Computer architecture, Stability analysis
BibRef
Zhang, K.[Kaihua],
Dong, M.L.[Ming-Liang],
Liu, B.[Bo],
Yuan, X.T.[Xiao-Tong],
Liu, Q.S.[Qing-Shan],
DeepACG: Co-Saliency Detection via Semantic-aware Contrast
Gromov-Wasserstein Distance,
CVPR21(13698-13707)
IEEE DOI
2111
Image segmentation, Correlation,
Image edge detection, Semantics, Network architecture, Benchmark testing
BibRef
Su, X.[Xiu],
Huang, T.[Tao],
Li, Y.X.[Yan-Xi],
You, S.[Shan],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Xu, C.[Chang],
Prioritized Architecture Sampling with Monto-Carlo Tree Search,
CVPR21(10963-10972)
IEEE DOI
2111
Training, Monte Carlo methods, Costs, Codes,
Computational modeling, Computer architecture
BibRef
Gao, S.H.[Shang-Hua],
Han, Q.[Qi],
Li, D.[Duo],
Cheng, M.M.[Ming-Ming],
Peng, P.[Pai],
Representative Batch Normalization with Feature Calibration,
CVPR21(8665-8675)
IEEE DOI
2111
Training, Costs, Neural networks, Standardization,
Calibration, Pattern recognition
BibRef
Li, S.[Sheng],
Tan, M.X.[Ming-Xing],
Pang, R.[Ruoming],
Li, A.[Andrew],
Cheng, L.Q.[Li-Qun],
Le, Q.V.[Quoc V.],
Jouppi, N.P.[Norman P.],
Searching for Fast Model Families on Datacenter Accelerators,
CVPR21(8081-8091)
IEEE DOI
2111
Convolutional codes, Convolution,
Computational modeling, Search methods, Parallel processing, Hardware
BibRef
Xu, L.[Lumin],
Guan, Y.[Yingda],
Jin, S.[Sheng],
Liu, W.T.[Wen-Tao],
Qian, C.[Chen],
Luo, P.[Ping],
Ouyang, W.L.[Wan-Li],
Wang, X.G.[Xiao-Gang],
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search,
CVPR21(16067-16076)
IEEE DOI
2111
Training, Costs, Pose estimation,
Computer architecture, Streaming media, Real-time systems
BibRef
Cui, Y.F.[Yu-Fei],
Liu, Z.Q.[Zi-Quan],
Li, Q.[Qiao],
Chan, A.B.[Antoni B.],
Xue, C.J.[Chun Jason],
Bayesian Nested Neural Networks for Uncertainty Calibration and
Adaptive Compression,
CVPR21(2392-2401)
IEEE DOI
2111
Training, Uncertainty, Computational modeling,
Neural networks, Data models, Bayes methods
BibRef
Su, X.[Xiu],
You, S.[Shan],
Wang, F.[Fei],
Qian, C.[Chen],
Zhang, C.S.[Chang-Shui],
Xu, C.[Chang],
BCNet: Searching for Network Width with Bilaterally Coupled Network,
CVPR21(2175-2184)
IEEE DOI
2111
Training, Search methods, Sociology, Refining, Stochastic processes,
Benchmark testing, Sampling methods
BibRef
Hosseini, R.[Ramtin],
Yang, X.Y.[Xing-Yi],
Xie, P.[Pengtao],
DSRNA: Differentiable Search of Robust Neural Architectures,
CVPR21(6192-6201)
IEEE DOI
2111
Measurement, Jacobian matrices, Deep learning, Training,
Perturbation methods, Computer architecture, Search problems
BibRef
Huang, S.Y.[Sian-Yao],
Chu, W.T.[Wei-Ta],
Searching by Generating: Flexible and Efficient One-Shot NAS with
Architecture Generator,
CVPR21(983-992)
IEEE DOI
2111
Training, Costs, Codes, Memory management,
Graphics processing units, Search problems
BibRef
Dai, X.L.[Xiao-Liang],
Wan, A.[Alvin],
Zhang, P.Z.[Pei-Zhao],
Wu, B.C.[Bi-Chen],
He, Z.J.[Zi-Jian],
Wei, Z.[Zhen],
Chen, K.[Kan],
Tian, Y.D.[Yuan-Dong],
Yu, M.[Matthew],
Vajda, P.[Peter],
Gonzalez, J.E.[Joseph E.],
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining,
CVPR21(16271-16280)
IEEE DOI
2111
Training, Search methods, Neural networks, Computer architecture,
Manuals, Performance gain, Prediction algorithms
BibRef
Xiong, Y.Y.[Yun-Yang],
Liu, H.X.[Han-Xiao],
Gupta, S.[Suyog],
Akin, B.[Berkin],
Bender, G.[Gabriel],
Wang, Y.Z.[Yong-Zhe],
Kindermans, P.J.[Pieter-Jan],
Tan, M.X.[Ming-Xing],
Singh, V.[Vikas],
Chen, B.[Bo],
MobileDets:
Searching for Object Detection Architectures for Mobile Accelerators,
CVPR21(3824-3833)
IEEE DOI
2111
Convolutional codes, Image edge detection, Neural networks,
Object detection, Computer architecture, Network architecture, Search problems
BibRef
He, Y.F.[Yu-Fan],
Yang, D.[Dong],
Roth, H.[Holger],
Zhao, C.[Can],
Xu, D.[Daguang],
DiNTS: Differentiable Neural Network Topology Search for 3D Medical
Image Segmentation,
CVPR21(5837-5846)
IEEE DOI
2111
Image segmentation, Solid modeling,
Network topology, Graphics processing units, Benchmark testing, Topology
BibRef
Ding, M.Y.[Ming-Yu],
Lian, X.C.[Xiao-Chen],
Yang, L.J.[Lin-Jie],
Wang, P.[Peng],
Jin, X.J.[Xiao-Jie],
Lu, Z.W.[Zhi-Wu],
Luo, P.[Ping],
HR-NAS: Searching Efficient High-Resolution Neural Architectures with
Lightweight Transformers,
CVPR21(2981-2991)
IEEE DOI
2111
Convolutional codes, Image segmentation, Computational modeling,
Computer architecture, Transformers, Search problems, Encoding
BibRef
Li, Y.[Yawei],
Li, W.[Wen],
Danelljan, M.[Martin],
Zhang, K.[Kai],
Gu, S.[Shuhang],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
The Heterogeneity Hypothesis:
Finding Layer-Wise Differentiated Network Architectures,
CVPR21(2144-2153)
IEEE DOI
2111
Training, Visualization, Protocols, Computational modeling,
Computer architecture, Network architecture, Pattern recognition
BibRef
Chen, M.H.[Ming-Hao],
Fu, J.L.[Jian-Long],
Ling, H.B.[Hai-Bin],
One-Shot Neural Ensemble Architecture Search by Diversity-Guided
Search Space Shrinking,
CVPR21(16525-16534)
IEEE DOI
2111
Codes, Computer architecture, Benchmark testing,
Extraterrestrial measurements, Robustness, Complexity theory
BibRef
Yan, B.[Bin],
Peng, H.[Houwen],
Wu, K.[Kan],
Wang, D.[Dong],
Fu, J.L.[Jian-Long],
Lu, H.C.[Hu-Chuan],
LightTrack: Finding Lightweight Neural Networks for Object Tracking
via One-Shot Architecture Search,
CVPR21(15175-15184)
IEEE DOI
2111
Oceans, Neural networks, Graphics processing units, Computer architecture,
Real-time systems
BibRef
Yan, Z.C.[Zhi-Cheng],
Dai, X.L.[Xiao-Liang],
Zhang, P.Z.[Pei-Zhao],
Tian, Y.D.[Yuan-Dong],
Wu, B.C.[Bi-Chen],
Feiszli, M.[Matt],
FP-NAS: Fast Probabilistic Neural Architecture Search,
CVPR21(15134-15143)
IEEE DOI
2111
Adaptation models, Computational modeling,
Memory management, Computer architecture, Probabilistic logic, Sampling methods
BibRef
Li, Z.G.[Zhen-Gang],
Yuan, G.[Geng],
Niu, W.[Wei],
Zhao, P.[Pu],
Li, Y.Y.[Yan-Yu],
Cai, Y.X.[Yu-Xuan],
Shen, X.[Xuan],
Zhan, Z.[Zheng],
Kong, Z.L.[Zheng-Lun],
Jin, Q.[Qing],
Chen, Z.Y.[Zhi-Yu],
Liu, S.J.[Si-Jia],
Yang, K.Y.[Kai-Yuan],
Ren, B.[Bin],
Wang, Y.Z.[Yan-Zhi],
Lin, X.[Xue],
NPAS: A Compiler-aware Framework of Unified Network Pruning and
Architecture Search for Beyond Real-Time Mobile Acceleration,
CVPR21(14250-14261)
IEEE DOI
2111
Training, Performance evaluation, Codes, Computational modeling,
Computer architecture, Reinforcement learning, Network architecture
BibRef
Zhang, X.[Xiong],
Xu, H.M.[Hong-Min],
Mo, H.[Hong],
Tan, J.C.[Jian-Chao],
Yang, C.[Cheng],
Wang, L.[Lei],
Ren, W.Q.[Wen-Qi],
DCNAS: Densely Connected Neural Architecture Search for Semantic
Image Segmentation,
CVPR21(13951-13962)
IEEE DOI
2111
Training, Image segmentation, Visualization,
Semantics, Memory management, Network architecture
BibRef
Yu, K.[Kaicheng],
Ranftl, R.[René],
Salzmann, M.[Mathieu],
Landmark Regularization: Ranking Guided Super-Net Training in Neural
Architecture Search,
CVPR21(13718-13727)
IEEE DOI
2111
Training, Correlation, Limiting,
Computational modeling, Computer architecture, Hardware
BibRef
Gu, Y.C.[Yu-Chao],
Wang, L.J.[Li-Juan],
Liu, Y.[Yun],
Yang, Y.[Yi],
Wu, Y.H.[Yu-Huan],
Lu, S.P.[Shao-Ping],
Cheng, M.M.[Ming-Ming],
DOTS: Decoupling Operation and Topology in Differentiable
Architecture Search,
CVPR21(12306-12315)
IEEE DOI
2111
Codes, Microprocessors, Image edge detection,
Computer architecture, US Department of Transportation, Search problems
BibRef
Zhang, X.[Xuanyang],
Hou, P.F.[Peng-Fei],
Zhang, X.Y.[Xiang-Yu],
Sun, J.[Jian],
Neural Architecture Search with Random Labels,
CVPR21(10902-10911)
IEEE DOI
2111
Training, Computer architecture,
Pattern recognition, Task analysis
BibRef
Yang, Z.H.[Zhao-Hui],
Wang, Y.H.[Yun-He],
Chen, X.[Xinghao],
Guo, J.Y.[Jian-Yuan],
Zhang, W.[Wei],
Xu, C.[Chao],
Xu, C.[Chunjing],
Tao, D.C.[Da-Cheng],
Xu, C.[Chang],
HourNAS: Extremely Fast Neural Architecture Search Through an
Hourglass Lens,
CVPR21(10891-10901)
IEEE DOI
2111
Deep learning, Costs, Neural networks, Graphics processing units,
Computer architecture, Complexity theory, Pattern recognition
BibRef
Liang, T.T.[Ting-Ting],
Wang, Y.T.[Yong-Tao],
Tang, Z.[Zhi],
Hu, G.S.[Guo-Sheng],
Ling, H.B.[Hai-Bin],
OPANAS: One-Shot Path Aggregation Network Architecture Search for
Object Detection,
CVPR21(10190-10198)
IEEE DOI
2111
Training, Visualization, Costs, Graphics processing units,
Object detection, Computer architecture, Network architecture
BibRef
Chen, Y.F.[Yao-Fo],
Guo, Y.[Yong],
Chen, Q.[Qi],
Li, M.L.[Min-Li],
Zeng, W.[Wei],
Wang, Y.W.[Yao-Wei],
Tan, M.[Mingkui],
Contrastive Neural Architecture Search with Neural Architecture
Comparators,
CVPR21(9497-9506)
IEEE DOI
2111
Training data, Computer architecture,
Pattern recognition, Computational efficiency, Task analysis
BibRef
Yang, Y.[Yibo],
You, S.[Shan],
Li, H.Y.[Hong-Yang],
Wang, F.[Fei],
Qian, C.[Chen],
Lin, Z.C.[Zhou-Chen],
Towards Improving the Consistency, Efficiency, and Flexibility of
Differentiable Neural Architecture Search,
CVPR21(6663-6672)
IEEE DOI
2111
Training, Costs, Error analysis, Search methods,
Memory management, Computer architecture
BibRef
Cai, S.F.[Shao-Fei],
Li, L.[Liang],
Deng, J.C.[Jin-Can],
Zhang, B.C.[Bei-Chen],
Zha, Z.J.[Zheng-Jun],
Su, L.[Li],
Huang, Q.M.[Qing-Ming],
Rethinking Graph Neural Architecture Search from Message-passing,
CVPR21(6653-6662)
IEEE DOI
2111
Filtering, Message passing, Computer architecture, Manuals,
Search problems, Feature extraction, Graph neural networks
BibRef
Wang, D.[Dilin],
Li, M.[Meng],
Gong, C.Y.[Cheng-Yue],
Chandra, V.[Vikas],
AttentiveNAS: Improving Neural Architecture Search via Attentive
Sampling,
CVPR21(6414-6423)
IEEE DOI
2111
Training, Codes, Computer architecture, Pattern recognition
BibRef
Duan, Y.[Yawen],
Chen, X.[Xin],
Xu, H.[Hang],
Chen, Z.W.[Ze-Wei],
Liang, X.D.[Xiao-Dan],
Zhang, T.[Tong],
Li, Z.G.[Zhen-Guo],
TransNAS-Bench-101: Improving transferability and Generalizability of
Cross-Task Neural Architecture Search,
CVPR21(5247-5256)
IEEE DOI
2111
Training, Knowledge engineering,
Design methodology, Transfer learning, Computer architecture, Benchmark testing
BibRef
Xu, Y.X.[Yi-Xing],
Wang, Y.H.[Yun-He],
Han, K.[Kai],
Tang, Y.H.[Ye-Hui],
Jui, S.L.[Shang-Ling],
Xu, C.J.[Chun-Jing],
Xu, C.[Chang],
ReNAS: Relativistic Evaluation of Neural Architecture Search,
CVPR21(4409-4418)
IEEE DOI
2111
Training, Performance evaluation, Tensors, Costs, Microprocessors,
Refining, Estimation
BibRef
Yang, T.J.[Tien-Ju],
Liao, Y.L.[Yi-Lun],
Sze, V.[Vivienne],
NetAdaptV2: Efficient Neural Architecture Search with Fast
Super-Network Training and Architecture Optimization,
CVPR21(2402-2411)
IEEE DOI
2111
Training, Measurement, Deep learning, Technological innovation,
Costs, Estimation, Computer architecture
BibRef
Chu, G.[Grace],
Arikan, O.[Okan],
Bender, G.[Gabriel],
Wang, W.J.[Wei-Jun],
Brighton, A.[Achille],
Kindermans, P.J.[Pieter-Jan],
Liu, H.X.[Han-Xiao],
Akin, B.[Berkin],
Gupta, S.[Suyog],
Howard, A.[Andrew],
Discovering Multi-Hardware Mobile Models via Architecture Search,
ECV21(3016-3025)
IEEE DOI
2109
Graphics processing units, Focusing,
Computer architecture, Debugging, Extraterrestrial measurements
BibRef
Peer, D.[David],
Stabinger, S.[Sebastian],
Rodríguez-Sánchez, A.[Antonio],
Conflicting Bundles: Adapting Architectures Towards the Improved
Training of Deep Neural Networks,
WACV21(256-265)
IEEE DOI
2106
Training, Measurement, Adaptation models,
Neural networks, Memory management
BibRef
Alt, T.[Tobias],
Peter, P.[Pascal],
Weickert, J.[Joachim],
Schrader, K.[Karl],
Translating Numerical Concepts for PDEs into Neural Architectures,
SSVM21(294-306).
Springer DOI
2106
BibRef
Tanveer, M.S.[Muhammad Suhaib],
Khan, M.U.K.[Muhammad Umar Karim],
Kyung, C.M.[Chong-Min],
Fine-Tuning DARTS for Image Classification,
ICPR21(4789-4796)
IEEE DOI
2105
Microprocessors, Computer architecture, Image classification
BibRef
Tsukahara, T.[Takuya],
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
BibRef
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Ma, H.L.[Hai-Long],
Xu, R.[Ruijun],
Li, Q.Y.[Qing-Yuan],
Fast, Accurate and Lightweight Super-Resolution with Neural
Architecture Search,
ICPR21(59-64)
IEEE DOI
2105
Training, Performance evaluation, PSNR, Image coding,
Superresolution, Computer architecture, Reinforcement learning
BibRef
Zhang, H.[Huigang],
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
BibRef
Jordao, A.[Artur],
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
BibRef
Ruiz, A.[Adria],
Agudo, A.[Antonio],
Moreno-Noguer, F.[Francesc],
Generating Attribution Maps with Disentangled Masked Backpropagation,
ICCV21(885-894)
IEEE DOI
2203
Backpropagation, Visualization, Computational modeling,
Piecewise linear approximation, Neural networks,
BibRef
López, J.G.[Javier García],
Agudo, A.[Antonio],
Moreno-Noguer, F.[Francesc],
E-DNAS: Differentiable Neural Architecture Search for Embedded
Systems,
ICPR21(4704-4711)
IEEE DOI
2105
Measurement, Training, Embedded systems, Search methods,
Presence network agents, System-on-chip, Kernel, Deep Learning,
Convolutional Meta Kernels
BibRef
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
BibRef
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
BibRef
Siddiqui, S.[Shahid],
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
BibRef
Donegan, C.[Ciarán],
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
BibRef
La Grassa, R.[Riccardo],
Gallo, I.[Ignazio],
Landro, N.[Nicola],
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft
Grained for Classification Task,
CAIP21(I:393-402).
Springer DOI
2112
BibRef
Gallo, I.[Ignazio],
Magistrali, G.,
Landro, N.[Nicola],
La Grassa, R.[Riccardo],
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.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
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
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, 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
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],
Chen, Y.P.[Yun-Peng],
Liu, S.[Si],
Tan, Z.X.[Zhen-Xiong],
Yan, S.C.[Shui-Cheng],
AdversarialNAS: Adversarial Neural Architecture Search for GANs,
CVPR20(5679-5688)
IEEE DOI
2008
Computer architecture, Generators, Task analysis,
Convolution, Generative adversarial networks
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.S.[Jia-Shi],
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
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).
HTML Version.
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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
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.H.[Zhao-Hui],
Wang, Y.H.[Yun-He],
Chen, X.H.[Xing-Hao],
Shi, B.X.[Bo-Xin],
Xu, C.[Chao],
Xu, C.J.[Chun-Jing],
Tian, Q.[Qi],
Xu, C.[Chang],
CARS: Continuous Evolution for Efficient Neural Architecture Search,
CVPR20(1826-1835)
IEEE DOI
2008
Optimization, Nickel, Computer architecture, Network architecture,
Sorting, Training, Automobiles
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks for Classification and Pattern Recognition .