14.5.9.3.1 Neural Architecture, Neural Architecture Search, NAS

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

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
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, 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, 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], 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 Utilization Ratio,
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, 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 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.R.[De-Rong], Zhu, T.W.[Tian-Wen], 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

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, 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 algorithm,
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], Xiang, S.M.[Shi-Ming], 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
Optimization, Learning (artificial intelligence), Task analysis, Acceleration, sparse optimization BibRef

Zhang, X.B.[Xin-Bang], Chang, J.L.[Jian-Long], Guo, Y.W.[Yi-Wen], 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
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
Training, Dynamic programming, Graphics processing units, Task analysis, dynamic programming BibRef

Xu, Y.H.[Yu-Hui], Xie, L.X.[Ling-Xi], Dai, W.R.[Wen-Rui], Zhang, X.P.[Xiao-Peng], Chen, X.[Xin], Qi, G.J.[Guo-Jun], Xiong, H.K.[Hong-Kai], Tian, Q.[Qi],
Partially-Connected Neural Architecture Search for Reduced Computational Redundancy,
PAMI(43), No. 9, September 2021, pp. 2953-2970.
IEEE DOI 2108
Redundancy, Network architecture, Stability analysis, Microprocessors, Space exploration, normalization 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
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
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.X.[Qi-Xiang],
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, Search problems, Task analysis, Decoding, Feature extraction, Detectors BibRef

Zhang, Z.[Zhen], Liu, S.H.[Shang-Hao], Zhang, Y.[Yang], Chen, W.B.[Wen-Bo],
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

Guo, Q.B.[Qing-Bei], 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, 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
Topology, Microprocessors, Benchmark testing, Training, Search problems, Deep learning, deep learning BibRef

Hu, Y.F.[Yu-Fei], Belkhir, N.[Nacim], Angulo, J.[Jesus], Yao, A.[Angela], Franchi, G.[Gianni],
Learning deep morphological networks with neural architecture search,
PR(131), 2022, pp. 108893.
Elsevier DOI 2208
Mathematical morphology, Deep learning, Architecture search, Edge detection, Semantic segmentation BibRef

Ren, X.H.[Xu-Hong], Chen, J.[Jianlang], Juefei-Xu, F.[Felix], Xue, W.L.[Wan-Li], Guo, Q.[Qing], Ma, L.[Lei], Zhao, J.J.[Jian-Jun], Chen, S.Y.[Sheng-Yong],
DARTSRepair: Core-failure-set guided DARTS for network robustness to common corruptions,
PR(131), 2022, pp. 108864.
Elsevier DOI 2208
Network architecture search, Core-failure-set selection, Robustness enhancement, Differentiable architecture search BibRef

Liu, J.Y.[Jin-Yuan], Wu, Y.H.[Yu-Hui], Wu, G.Y.[Guan-Yao], Liu, R.S.[Ri-Sheng], Fan, X.[Xin],
Learn to Search a Lightweight Architecture for Target-Aware Infrared and Visible Image Fusion,
SPLetters(29), 2022, pp. 1614-1618.
IEEE DOI 2208
Training, Image fusion, Task analysis, Search problems, Feature extraction, Fuses, Deep learning, neural architecture search BibRef

Wang, L.[Linnan], Xie, S.[Saining], Li, T.[Teng], Fonseca, R.[Rodrigo], Tian, Y.D.[Yuan-Dong],
Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search,
PAMI(44), No. 9, September 2022, pp. 5503-5515.
IEEE DOI 2208
Vegetation, Optimization, Measurement, Bayes methods, Task analysis, Search problems, Monte Carlo methods, Neural architecture search, Monte Carlo tree search BibRef

Wang, Y.[Yu], Li, Y.S.[Yan-Sheng], Chen, W.[Wei], Li, Y.Z.[Yun-Zhou], Dang, B.[Bo],
DNAS: Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tian, Y.[Yuesong], Shen, L.[Li], Shen, L.[Li], Su, G.[Guinan], Li, Z.F.[Zhi-Feng], Liu, W.[Wei],
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks,
PAMI(44), No. 10, October 2022, pp. 6752-6766.
IEEE DOI 2209
Generators, Search problems, Generative adversarial networks, Training, Nash equilibrium, generative models BibRef

Tong, L.Y.[Lyu-Yang], Du, B.[Bo],
Neural architecture search via reference point based multi-objective evolutionary algorithm,
PR(132), 2022, pp. 108962.
Elsevier DOI 2209
Neural architecture search, Multi-objective evolutionary algorithm, The image classification BibRef

Shen, H.[Hao], Zhao, Z.Q.[Zhong-Qiu], Liao, W.R.[Wen-Rui], Tian, W.D.[Wei-Dong], Huang, D.S.[De-Shuang],
Joint operation and attention block search for lightweight image restoration,
PR(132), 2022, pp. 108909.
Elsevier DOI 2209
Image restoration, Neural architecture search, Attention mechanism BibRef

Yu, K.C.[Kai-Cheng], Ranftl, R.[René], Salzmann, M.[Mathieu],
An Analysis of Super-Net Heuristics in Weight-Sharing NAS,
PAMI(44), No. 11, November 2022, pp. 8110-8124.
IEEE DOI 2210
BibRef
Earlier:
Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search,
CVPR21(13718-13727)
IEEE DOI 2111
Training, Protocols, Task analysis, Measurement, Benchmark testing, Encoding, AutoML, neural architecture search, weight-sharing, super-net. Correlation, Limiting, Computational modeling, Hardware BibRef

Liu, Z.J.[Zhi-Jian], Tang, H.T.[Hao-Tian], Zhao, S.Y.[Sheng-Yu], Shao, K.[Kevin], Han, S.[Song],
PVNAS: 3D Neural Architecture Search With Point-Voxel Convolution,
PAMI(44), No. 11, November 2022, pp. 8552-8568.
IEEE DOI 2210
Convolution, Solid modeling, Random access memory, Computational modeling, Memory management, Neural networks, autonomous driving 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

Yu, H.Y.[Hong-Yuan], Peng, H.[Houwen], Huang, Y.[Yan], Fu, J.L.[Jian-Long], Du, H.[Hao], Wang, L.[Liang], Ling, H.B.[Hai-Bin],
Cyclic Differentiable Architecture Search,
PAMI(45), No. 1, January 2023, pp. 211-228.
IEEE DOI 2212
Computer architecture, Optimization, Search problems, Task analysis, Training, Microprocessors, Object detection, Cyclic, unified framework BibRef

Gudzius, P.[Povilas], Kurasova, O.[Olga], Darulis, V.[Vytenis], Filatovas, E.[Ernestas],
AutoML-Based Neural Architecture Search for Object Recognition in Satellite Imagery,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Zhang, Y.Q.[Yong-Qi], Yao, Q.M.[Quan-Ming], Kwok, J.T.[James T.],
Bilinear Scoring Function Search for Knowledge Graph Learning,
PAMI(45), No. 2, February 2023, pp. 1458-1473.
IEEE DOI 2301
Task analysis, Artificial neural networks, Training, Machine learning, Evolutionary computation, neural architecture search BibRef

Fu, S.[Siming], Chu, H.[Huanpeng], Yu, L.[Lu], Peng, B.[Bo], Li, Z.[Zheyang], Tan, W.M.[Wen-Ming], Hu, H.J.[Hao-Ji],
AuxBranch: Binarization residual-aware network design via auxiliary branch search,
PR(136), 2023, pp. 109263.
Elsevier DOI 2301
Binary neural network, Binarization residual, Performance estimation indicator, Neural architecture search BibRef

Wang, W.[Wenna], Zhang, X.W.[Xiu-Wei], Cui, H.F.[Heng-Fei], Yin, H.L.[Han-Lin], Zhang, Y.N.[Yan-Nnig],
FP-DARTS: Fast parallel differentiable neural architecture search for image classification,
PR(136), 2023, pp. 109193.
Elsevier DOI 2301
Neural architecture search, Computing overheads, Operator sub-sets, Two-parallel-path, Binary gate, Sigmoid function BibRef

Li, W.[Wei], Gong, S.G.[Shao-Gang], Zhu, X.T.[Xia-Tian],
Neural operator search,
PR(136), 2023, pp. 109215.
Elsevier DOI 2301
Neural architecture search, Search space, Self-calibration operations, Dynamic convolution, Knowledge distillation BibRef

Zheng, X.[Xiawu], Yang, C.Y.[Chen-Yi], Zhang, S.[Shaokun], Wang, Y.[Yan], Zhang, B.C.[Bao-Chang], Wu, Y.J.[Yong-Jian], Wu, Y.S.[Yun-Sheng], Shao, L.[Ling], Ji, R.R.[Rong-Rong],
DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning,
IJCV(131), No. 5, May 2023, pp. 1234-1249.
Springer DOI 2305
BibRef

Ao, L.[Lei], Feng, K.[Kaiyuan], Sheng, K.[Kai], Zhao, H.Y.[Hong-Yu], He, X.[Xin], Chen, Z.[Zigang],
TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification,
RS(15), No. 8, 2023, pp. 2212.
DOI Link 2305
BibRef

Li, Y.X.[Yan-Xi], Dong, M.J.[Min-Jing], Wang, Y.H.[Yun-He], Xu, C.[Chang],
Neural Architecture Search via Proxy Validation,
PAMI(45), No. 6, June 2023, pp. 7595-7610.
IEEE DOI 2305
Optimization, Training, Search problems, Graphics processing units, Costs, Predictive models, Neural architecture search, deep neural architecture BibRef

Su, X.[Xiu], You, S.[Shan], Xie, J.[Jiyang], Wang, F.[Fei], Qian, C.[Chen], Zhang, C.S.[Chang-Shui], Xu, C.[Chang],
Searching for Network Width With Bilaterally Coupled Network,
PAMI(45), No. 7, July 2023, pp. 8936-8953.
IEEE DOI 2306
BibRef
Earlier: A1, A2, A4, A5, A6, A7, Only:
BCNet: Searching for Network Width with Bilaterally Coupled Network,
CVPR21(2175-2184)
IEEE DOI 2111
Training, Hardware, Benchmark testing, Search methods, Neural networks, Convolutional neural networks, Sociology, stochastic complementary strategy. Refining, Stochastic processes, Sampling methods BibRef

Huang, H.[Han], Shen, L.[Li], He, C.Y.[Chao-Yang], Dong, W.S.[Wei-Sheng], Liu, W.[Wei],
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-Resolution,
CirSysVideo(33), No. 6, June 2023, pp. 2672-2682.
IEEE DOI 2306
Task analysis, Convolution, Superresolution, Computational modeling, Search problems, Reinforcement learning, lightweight model design BibRef

Mohan, R.[Rohit], Elsken, T.[Thomas], Zela, A.[Arberf], Metzen, J.H.[Jan Hendrik], Staffler, B.[Benedikt], Brox, T.[Thomas], Valada, A.[Abhinav], Hutter, F.[Frank],
Neural Architecture Search for Dense Prediction Tasks in Computer Vision,
IJCV(131), No. 7, July 2023, pp. 1784-1807.
Springer DOI 2307
BibRef

Kang, X.T.[Xia-Tao], Li, P.[Ping], Yao, J.Y.[Jia-Yi], Li, C.X.[Cheng-Xi],
Neural Network Panning: Screening the Optimal Sparse Network Before Training,
ACCV22(I:602-617).
Springer DOI 2307
BibRef

Dou, Z.[Ziwen], Ye, D.[Dong], Wang, B.[Boya],
AutoSegEdge: Searching for the edge device real-time semantic segmentation based on multi-task learning,
IVC(136), 2023, pp. 104719.
Elsevier DOI 2308
Semantic segmentation, Multi-task-learning, Hardware-aware neural architecture search, Edge, Real-time BibRef

Xu, P.[Peng], Wang, K.[Ke], Hassan, M.M.[Mohammad Mehedi], Chen, C.M.[Chien-Ming], Lin, W.G.[Wei-Guo], Hassan, M.R.[Md. Rafiul], Fortino, G.[Giancarlo],
Adversarial Robustness in Graph-Based Neural Architecture Search for Edge AI Transportation Systems,
ITS(24), No. 8, August 2023, pp. 8465-8474.
IEEE DOI 2308
Robustness, Computational modeling, Data models, Mathematical models, Analytical models, Deep learning, model compression and neural architecture search BibRef

Wang, R.Q.[Run-Qi], Yang, L.L.[Lin-Lin], Chen, H.L.[Han-Lin], Wang, W.[Wei], Doermann, D.[David], Zhang, B.C.[Bao-Chang],
Anti-Bandit for Neural Architecture Search,
IJCV(131), No. 10, October 2023, pp. 2682-2698.
Springer DOI 2309
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
Logic gates, Artificial neural networks, Training, Encoding, Measurement, Data processing, Neural architecture search, ranking loss BibRef

Chen, Z.H.[Zhi-Hua], Qiu, G.[Guhao], Li, P.[Ping], Zhu, L.[Lei], Yang, X.K.[Xiao-Kang], Sheng, B.[Bin],
MNGNAS: Distilling Adaptive Combination of Multiple Searched Networks for One-Shot Neural Architecture Search,
PAMI(45), No. 11, November 2023, pp. 13489-13508.
IEEE DOI 2310
BibRef

Wang, X.X.[Xiao-Xing], Lian, Z.[Zhirui], Lin, J.[Jiale], Xue, C.[Chao], Yan, J.C.[Jun-Chi],
DIY Your EasyNAS for Vision: Convolution Operation Merging, Map Channel Reducing, and Search Space to Supernet Conversion Tooling,
PAMI(45), No. 11, November 2023, pp. 13974-13990.
IEEE DOI 2310
BibRef

Wang, X.X.[Xiao-Xing], Lin, J.[Jiale], Zhao, J.P.[Juan-Ping], Yang, X.K.[Xiao-Kang], Yan, J.C.[Jun-Chi],
EAutoDet: Efficient Architecture Search for Object Detection,
ECCV22(XX:668-684).
Springer DOI 2211
BibRef

Li, Y.J.[Yan-Jing], Xu, S.[Sheng], Cao, X.B.[Xian-Bin], Zhuo, L.[Li'an], Zhang, B.C.[Bao-Chang], Wang, T.[Tian], Guo, G.D.[Guo-Dong],
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs,
IJCV(131), No. 1, January 2023, pp. 2793-2815.
Springer DOI 2310
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, Recognition and classification, Optimization and learning methods BibRef

Qian, X.X.[Xiao-Xue], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Zhang, X.R.[Xiang-Rong], Huang, X.Y.[Xin-Yan], Li, S.[Shuo], Chen, P.[Puhua], Liu, X.[Xu],
Knowledge transfer evolutionary search for lightweight neural architecture with dynamic inference,
PR(143), 2023, pp. 109790.
Elsevier DOI 2310
Neural architecture search (NAS), Knowledge transfer, Dynamic inference, Image classification BibRef

Ma, B.[Benteng], Zhang, J.[Jing], Xia, Y.[Yong], Tao, D.C.[Da-Cheng],
Inter-layer transition in neural architecture search,
PR(143), 2023, pp. 109697.
Elsevier DOI 2310
Image classification, Neural network, Neural architecture search BibRef

Liu, A.[Aishan], Tang, S.Y.[Shi-Yu], Liang, S.Y.[Si-Yuan], Gong, R.[Ruihao], Wu, B.[Boxi], Liu, X.L.[Xiang-Long], Tao, D.C.[Da-Cheng],
Exploring the Relationship Between Architectural Design and Adversarially Robust Generalization,
CVPR23(4096-4107)
IEEE DOI 2309
BibRef

Xie, B.Q.[Bang-Quan], Yang, Z.M.[Zong-Ming], Yang, L.[Liang], Luo, R.[Ruifa], Lu, J.[Jun], Wei, A.[Ailin], Weng, X.X.[Xiao-Xiong], Li, B.[Bing],
ANAS: Asymptotic NAS for large-scale proxyless search and multi-task transfer learning,
PR(144), 2023, pp. 109821.
Elsevier DOI 2310
Neural architecture search, Memory consumption, Proxyless search, Multi-task transfer, Classification and segmentation BibRef

Poyser, M.[Matt], Breckon, T.P.[Toby P.],
Neural architecture search: A contemporary literature review for computer vision applications,
PR(147), 2024, pp. 110052.
Elsevier DOI 2312
Neural architecture search, Classification, Detection, Segmentation BibRef

Heuillet, A.[Alexandre], Tabia, H.[Hedi], Arioui, H.[Hichem], Youcef-Toumi, K.[Kamal],
D-DARTS: Distributed Differentiable Architecture Search,
PRL(176), 2023, pp. 42-48.
Elsevier DOI 2312
Neural architecture search, Differentiable architecture search, Deep learning, Computer vision BibRef

Chen, W.Y.[Wu-Yang], Gong, X.Y.[Xin-Yu], Wu, J.[Junru], Wei, Y.C.[Yun-Chao], Shi, H.[Humphrey], Yan, Z.C.[Zhi-Cheng], Yang, Y.[Yi], Wang, Z.Y.[Zhang-Yang],
Understanding and Accelerating Neural Architecture Search With Training-Free and Theory-Grounded Metrics,
PAMI(46), No. 2, February 2024, pp. 749-763.
IEEE DOI 2401
Generalization, linear region, neural architecture search, neural tangent kernel BibRef

Gong, X.Y.[Xin-Yu], Chang, S.Y.[Shi-Yu], Jiang, Y.F.[Yi-Fan], Wang, Z.Y.[Zhang-Yang],
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

Wang, G.R.[Guang-Run], Li, C.L.[Chang-Lin], Yuan, L.C.[Liu-Chun], Peng, J.F.[Jie-Feng], Xian, X.Y.[Xiao-Yu], Liang, X.D.[Xiao-Dan], Chang, X.J.[Xiao-Jun], Lin, L.[Liang],
DNA Family: Boosting Weight-Sharing NAS With Block-Wise Supervisions,
PAMI(46), No. 5, May 2024, pp. 2722-2740.
IEEE DOI 2404
Computer architecture, DNA, Training, Computational modeling, Optimization, Transformers, Scalability, Block-wise learning, vision transformer BibRef

Liao, Y.G.[Yu-Gang], Li, J.Q.[Jun-Qing], Wei, S.W.[Shu-Wei], Xiao, X.M.[Xiu-Mei],
Evolutionary Search via channel attention based parameter inheritance and stochastic uniform sampled training,
CVIU(243), 2024, pp. 104000.
Elsevier DOI 2405
Deep learning, Neural architecture search, Image classification BibRef

Guo, B.C.[Bi-Cheng], Xu, L.[Lilin], Chen, T.[Tao], Ye, P.[Peng], He, S.[Shibo], Liu, H.Y.[Hao-Yu], Chen, J.M.[Ji-Ming],
Latency-Aware Neural Architecture Performance Predictor With Query-to-Tier Technique,
CirSysVideo(34), No. 7, July 2024, pp. 5868-5883.
IEEE DOI 2407
Computer architecture, Training, Costs, Prediction algorithms, Performance evaluation, Pipelines, Search problems, latency BibRef

Ho, J.C.[Jia-Cang], Park, K.[Kyongseok], Kang, D.K.[Dae-Ki],
GLNAS: Greedy Layer-wise Network Architecture Search for low cost and fast network generation,
PR(155), 2024, pp. 110730.
Elsevier DOI 2408
Automated machine learning, Greedy layer-wise, Network architecture search, Image classification BibRef

Ma, B.T.[Ben-Teng], Zhang, Y.N.[Yan-Ning], Xia, Y.[Yong],
Momentum recursive DARTS,
PR(156), 2024, pp. 110710.
Elsevier DOI 2408
Neural architecture search, Gradient estimation, Image recognition BibRef

Lin, C.H.[Chuen-Horng], Chen, T.Y.[Tsung-Yi], Chen, H.Y.[Huan-Yu], Chan, Y.K.[Yung-Kuan],
Efficient and lightweight convolutional neural network architecture search methods for object classification,
PR(156), 2024, pp. 110752.
Elsevier DOI Code:
WWW Link. 2408
Convolutional neural network (CNN), Architecture search, Lightweight, Long short-term memory (LSTM) network BibRef

Ma, B.T.[Ben-Teng], Zhang, J.[Jing], Xia, Y.[Yong], Tao, D.C.[Da-Cheng],
VNAS: Variational Neural Architecture Search,
IJCV(132), No. 1, January 2024, pp. 3689-3713.
Springer DOI 2409
BibRef

Zhou, Q.Q.[Qin-Qin], Sheng, K.[Kekai], Zheng, X.[Xiawu], Li, K.[Ke], Tian, Y.H.[Yong-Hong], Chen, J.[Jie], Ji, R.R.[Rong-Rong],
Training-Free Transformer Architecture Search With Zero-Cost Proxy Guided Evolution,
PAMI(46), No. 10, October 2024, pp. 6525-6541.
IEEE DOI 2409
Transformers, Computer architecture, Graphics processing units, Task analysis, Architecture, Correlation, Evolution, transformer BibRef

Wang, A.[Aili], Zhang, K.[Kang], Wu, H.B.[Hai-Bin], Dai, S.Y.[Shi-Yu], Iwahori, Y.[Yuji], Yu, X.Y.[Xiao-Yu],
Noise-Disruption-Inspired Neural Architecture Search with Spatial-Spectral Attention for Hyperspectral Image Classification,
RS(16), No. 17, 2024, pp. 3123.
DOI Link 2409
BibRef

Artaud, C.[Corentin], De-Silva, V.[Varuna], Pina, R.[Rafael], Shi, X.[Xiyu],
Generating neural architectures from parameter spaces for multi-agent reinforcement learning,
PRL(185), 2024, pp. 272-278.
Elsevier DOI 2410
Multi-agent reinforcement learning, Generative models, Neural networks, Transformers, Parameter generation BibRef

Turner, J.[Jack], Crowley, E.J.[Elliot J.], O'Boyle, M.F.P.[Michael F.P.],
Neural Architecture Search as Program Transformation Exploration,
CACM(67), No. 10, October 2024, pp. 92-100.
DOI Link 2410
In this work, we express neural architecture operations as program transformations whose legality depends on a notion of representational capacity. BibRef

Chen, Y.F.[Yao-Fo], Guo, Y.[Yong], Liao, D.H.[Dai-Hai], Lv, F.B.[Fan-Bing], Song, H.J.[Heng-Jie], Kwok, J.T.Y.[James Tin-Yau], Tan, M.K.[Ming-Kui],
Automated Dominative Subspace Mining for Efficient Neural Architecture Search,
CirSysVideo(34), No. 10, October 2024, pp. 9281-9297.
IEEE DOI 2411
Computer architecture, Statistics, Sociology, Costs, Circuits and systems, Training, Technological innovation, convolutional neural networks 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.K.[Ming-Kui],
Contrastive Neural Architecture Search with Neural Architecture Comparators,
CVPR21(9497-9506)
IEEE DOI 2111
Training data, Computational efficiency, Task analysis BibRef

Li, G.H.[Gui-Hong], Hoang, D.[Duc], Bhardwaj, K.[Kartikeya], Lin, M.[Ming], Wang, Z.Y.[Zhang-Yang], Marculescu, R.[Radu],
Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities,
PAMI(46), No. 12, December 2024, pp. 7618-7635.
IEEE DOI 2411
Training, Computer architecture, Hardware, Costs, Benchmark testing, Computational modeling, Vectors, Neural architecture search, hardware-aware neural network design BibRef

Zhang, Y.M.[Yu-Ming], Hsieh, J.W.[Jun-Wei], Chang, Y.H.[Yu-Hsiu], Li, X.[Xin], Chang, M.C.[Ming-Ching], Lee, C.C.[Chun-Chieh], Fan, K.C.[Kuo-Chin],
Set-Nas: Sample-Efficient Training for Neural Architecture Search With Strong Predictor and Stratified Sampling,
ICIP24(680-686)
IEEE DOI 2411
Training, Performance evaluation, Costs, Graph convolutional networks, Network architecture, efficient NAS BibRef

Zhang, T.[Tunhou], Li, S.Y.[Shi-Yu], Cheng, H.P.[Hsin-Pai], Yan, F.[Feng], Li, H.[Hai], Chen, Y.[Yiran],
CSCO: Connectivity Search of Convolutional Operators,
NAS24(1685-1694)
IEEE DOI 2410
Convolutional codes, Wiring, Architecture, Buildings, Computer architecture, Data augmentation, Convolutional Neural Networks BibRef

Jiang, J.T.[Jian-Tong], Wen, Z.Y.[Ze-Yi], Mansoor, A.[Atif], Mian, A.[Ajmal],
Efficient Hyperparameter Optimization with Adaptive Fidelity Identification,
CVPR24(26181-26190)
IEEE DOI 2410
Adaptation models, Source coding, Computer architecture, Hyperparameter optimization, Bayes methods, Muti-fidelity BibRef

Lee, J.[Junghyup], Ham, B.[Bumsub],
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search,
CVPR24(5893-5903)
IEEE DOI 2410
Correlation, Runtime, Costs, Computer architecture, Network architecture, network architecture search BibRef

Li, Z.G.[Zhen-Gang], Kang, Y.[Yan], Liu, Y.C.[Yu-Chen], Liu, D.[Difan], Hinz, T.[Tobias], Liu, F.[Feng], Wang, Y.Z.[Yan-Zhi],
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model,
CVPR24(8661-8670)
IEEE DOI 2410
Training, Costs, Computational modeling, Search methods, Computer architecture, Network architecture, Diffusion Model, NAS, Model Efficiency BibRef

Broni-Bediako, C.[Clifford], Xia, J.[Junshi], Yokoya, N.[Naoto],
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping,
EarthVision24(543-553)
IEEE DOI Code:
WWW Link. 2410
Limiting, Neural networks, Land surface, Computer architecture, Benchmark testing, Search problems, Remote Sensing Image Segmentation BibRef

Geada, R.[Rob], Towers, D.[David], Forshaw, M.[Matthew], Atapour-Abarghouei, A.[Amir], McGough, A.S.[A. Stephen],
Insights from the Use of Previously Unseen Neural Architecture Search Datasets,
CVPR24(22541-22550)
IEEE DOI 2410
Deep learning, Knowledge engineering, Costs, Neural networks, Benchmark testing, NAS, Classification BibRef

Hwang, D.[Dongyeong], Kim, H.[Hyunju], Kim, S.[Sunwoo], Shin, K.[Kijung],
FlowerFormer: Empowering Neural Architecture Encoding Using a Flow-Aware Graph Transformer,
CVPR24(6128-6137)
IEEE DOI 2410
Training, Representation learning, Computational modeling, Message passing, Computer architecture, Speech recognition, Architecture Performance Prediction BibRef

Ou, Y.W.[Yu-Wei], Feng, Y.Q.[Yu-Qi], Sun, Y.[Yanan],
Towards Accurate and Robust Architectures via Neural Architecture Search,
CVPR24(5967-5976)
IEEE DOI 2410
Training, Accuracy, Closed box, Computer architecture, Search problems, Robustness BibRef

Mills, K.G.[Keith G.], Han, F.X.[Fred X.], Salameh, M.[Mohammad], Lu, S.Y.[Sheng-Yao], Zhou, C.H.[Chun-Hua], He, J.[Jiao], Sun, F.Y.[Feng-Yu], Niu, D.[Di],
Building Optimal Neural Architectures Using Interpretable Knowledge,
CVPR24(5726-5735)
IEEE DOI Code:
WWW Link. 2410
Image segmentation, Architecture, Neural networks, Focusing, Computer architecture, Extraterrestrial measurements, MobileNets BibRef

Zhang, B.[Beichen], Wang, X.X.[Xiao-Xing], Qin, X.H.[Xiao-Han], Yan, J.C.[Jun-Chi],
Boosting Order-Preserving and Transferability for Neural Architecture Search: A Joint Architecture Refined Search and Fine-Tuning Approach,
CVPR24(5662-5671)
IEEE DOI 2410
Training, Estimation, Computer architecture, Search problems, Boosting BibRef

Gao, T.X.[Tian-Xiao], Guo, L.[Li], Zhao, S.W.[Shan-Wei], Xu, P.[Peihan], Yang, Y.K.[Yu-Kun], Liu, X.[Xionghao], Wang, S.H.[Shi-Hao], Zhu, S.[Shiai], Zhou, D.J.[Da-Jiang],
QuantNAS: Quantization-aware Neural Architecture Search For Efficient Deployment On Mobile Device,
NAS24(1704-1713)
IEEE DOI 2410
Training, Quantization (signal), Computational modeling, Computer architecture, Mobile handsets, Deep learning BibRef

An, T.[Taegun], Joo, C.H.[Chang-Hee],
CycleGANAS: Differentiable Neural Architecture Search for CycleGAN,
NAS24(1655-1664)
IEEE DOI Code:
WWW Link. 2410
Dimensionality reduction, Search methods, Stacking, Neural networks, Computer architecture, CycleGAN BibRef

Subbotko, K.[Konstanty], Jablonski, W.[Wojciech], Bilinski, P.[Piotr],
The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol,
NAS24(1665-1674)
IEEE DOI 2410
Training, Protocols, Neural networks, DNA, Graphics processing units, Computer architecture, Neural Architecture Search, NAS, DARTS BibRef

Huang, Y.C.[Yi-Cheng], Li, W.H.[Wei-Hua], Tsou, C.H.[Chih-Han], Chen, J.C.[Jun-Cheng], Chen, C.S.[Chu-Song],
UP-NAS: Unified Proxy for Neural Architecture Search,
NAS24(1675-1684)
IEEE DOI Code:
WWW Link. 2410
Training, Codes, Neural networks, Computer architecture BibRef

Pham, C.[Chau], Teterwak, P.[Piotr], Nelson, S.[Soren], Plummer, B.A.[Bryan A.],
MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters,
WACV24(2788-2797)
IEEE DOI Code:
WWW Link. 2404
Codes, Computational modeling, Noise, Training data, Computer architecture, Artificial neural networks, Algorithms, Image recognition and understanding BibRef

Teterwak, P.[Piotr], Nelson, S.[Soren], Dryden, N.[Nikoli], Bashkirova, D.[Dina], Saenko, K.[Kate], Plummer, B.A.[Bryan A.],
Learning to Compose SuperWeights for Neural Parameter Allocation Search,
WACV24(2739-2748)
IEEE DOI 2404
Training, Weight measurement, Computational modeling, Computer architecture, Network architecture, Size measurement BibRef

Sinha, N.[Nilotpal], Rostami, P.[Peyman], El Rahman-Shabayek, A.[Abd], Kacem, A.[Anis], Aouada, D.[Djamila],
Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity,
ECV24(8032-8039)
IEEE DOI 2410
Performance evaluation, Costs, Image edge detection, Computer architecture, Search problems, Hardware, EdgeAI BibRef

Sinha, N.[Nilotpal], El Rahman-Shabayek, A.[Abd], Kacem, A.[Anis], Rostami, P.[Peyman], Shneider, C.[Carl], Aouada, D.[Djamila],
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric,
WACV24(2616-2625)
IEEE DOI 2404
Measurement, Training, Costs, Neural networks, Computer architecture, Search problems, Algorithms, Machine learning architectures BibRef

Chang, C.C.[Chi-Chih], Sung, Y.Y.[Yuan-Yao], Yu, S.X.[Shi-Xing], Huang, N.C.[Ning-Chi], Marculescu, D.[Diana], Wu, K.C.A.[Kai-Chi-Ang],
FLORA: Fine-grained Low-Rank Architecture Search for Vision Transformer,
WACV24(2470-2479)
IEEE DOI Code:
WWW Link. 2404
Training, Filtering, Flora, Computer architecture, Interference, Transformers, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Yoshihama, Y.[Yutaka], Yadani, K.[Kenichi], Isobe, S.[Shota],
Hardware-Aware Zero-Shot Neural Architecture Search,
MVA23(1-5)
DOI Link 2403
Machine vision, Computer architecture, Search problems, Hardware, Computational efficiency, Convolutional neural networks, Low latency communication BibRef

Wang, X.D.[Xu-Dong], Zhang, L.L.[Li Lyna], Xu, J.H.[Jia-Hang], Zhang, Q.L.[Quan-Lu], Wang, Y.J.[Yu-Jing], Yang, Y.Q.[Yu-Qing], Zheng, N.X.[Ning-Xin], Cao, T.[Ting], Yang, M.[Mao],
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference,
ICCV23(5796-5805)
IEEE DOI 2401
BibRef

Chu, X.X.[Xiang-Xiang], Lu, S.[Shun], Li, X.D.[Xu-Dong], Zhang, B.[Bo],
MixPath: A Unified Approach for One-shot Neural Architecture Search,
ICCV23(5949-5958)
IEEE DOI 2401
BibRef

Li, Y.H.[Yu-Hong], Li, J.J.[Jia-Jie], Hao, C.[Cong], Li, P.[Pan], Xiong, J.J.[Jin-Jun], Chen, D.[Deming],
Extensible and Efficient Proxy for Neural Architecture Search,
ICCV23(6176-6187)
IEEE DOI Code:
WWW Link. 2401
BibRef

Priyadarshi, S.[Sweta], Jiang, T.Y.[Tian-Yu], Cheng, H.P.[Hsin-Pai], Krishna, S.[Sendil], Ganapathy, V.[Viswanath], Patel, C.[Chirag],
DONNAv2: Lightweight Neural Architecture Search for Vision tasks,
REDLCV23(1376-1384)
IEEE DOI 2401
BibRef

Zhang, M.Y.[Ming-Yang], Yu, X.Y.[Xin-Yi], Zhao, H.D.[Hao-Dong], Ou, L.L.[Lin-Lin],
ShiftNAS: Improving One-shot NAS via Probability Shift,
ICCV23(5896-5905)
IEEE DOI 2401
BibRef

Wang, X.X.[Xiao-Xing], Chu, X.X.[Xiang-Xiang], Fan, Y.[Yuda], Zhang, Z.[Zhexi], Zhang, B.[Bo], Yang, X.K.[Xiao-Kang], Yan, J.C.[Jun-Chi],
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation,
ICCV23(5916-5926)
IEEE DOI 2401
BibRef

Addad, Y.[Youva], Lechervy, A.[Alexis], Jurie, F.[Frédéric],
Multi-Exit Resource-Efficient Neural Architecture for Image Classification with Optimized Fusion Block,
REDLCV23(1478-1483)
IEEE DOI 2401
BibRef

Sridhar, S.N.[Sharath Nittur], Kundu, S.[Souvik], Sundaresan, S.[Sairam], Szankin, M.[Maciej], Sarah, A.[Anthony],
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning,
REDLCV23(1515-1519)
IEEE DOI 2401
BibRef

García, J.L.L.[Jesús Leopoldo Llano], Monroy, R.[Raúl], Hernández, V.A.S.[Víctor Adrián Sosa],
An Experimental Protocol for Neural Architecture Search in Super-Resolution,
LXCV-ICCV23(4141-4148)
IEEE DOI 2401
BibRef

Sun, Z.H.[Zi-Hao], Sun, Y.[Yu], Yang, L.X.[Long-Xing], Lu, S.[Shun], Mei, J.L.[Ji-Lin], Zhao, W.X.[Wen-Xiao], Hu, Y.[Yu],
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS,
ICCV23(5740-5750)
IEEE DOI Code:
WWW Link. 2401
BibRef

Bhardwaj, K.[Kartikeya], Cheng, H.P.[Hsin-Pai], Priyadarshi, S.[Sweta], Li, Z.[Zhuojin],
ZiCo-BC: A Bias Corrected Zero-Shot NAS for Vision Tasks,
REDLCV23(1345-1349)
IEEE DOI 2401
BibRef

Cavagnero, N.[Niccolò], Robbiano, L.[Luca], Pistilli, F.[Francesca], Caputo, B.[Barbara], Averta, G.[Giuseppe],
Entropic Score metric: Decoupling Topology and Size in Training-free NAS,
REDLCV23(1451-1460)
IEEE DOI 2401
BibRef

Soro, B.[Bedionita], Song, C.[Chong],
Enhancing Differentiable Architecture Search: A Study on Small Number of Cell Blocks in the Search Stage, and Important Branches-based Cells Selection,
REDLCV23(1245-1253)
IEEE DOI 2401
BibRef

Siddiqui, S.[Shahid], Kyrkou, C.[Christos], Theocharides, T.[Theocharis],
True Rank Guided Efficient Neural Architecture Search for End to End Low-complexity Network Discovery,
CAIP23(I:25-34).
Springer DOI 2312
BibRef

Wei, Z.M.[Zi-Mian], Pan, H.Y.[Heng-Yue], Li, L.[Lujun], Dong, P.[Peijie], Niu, X.[Xin], Li, D.S.[Dong-Sheng],
MENAS: Multi-trial Evolutionary Neural Architecture Search with Lottery Tickets,
ICIP23(3379-3383)
IEEE DOI 2312
BibRef

Lu, S.[Shun], Hu, Y.[Yu], Yang, L.X.[Long-Xing], Sun, Z.H.[Zi-Hao], Mei, J.L.[Ji-Lin], Tan, J.C.[Jian-Chao], Song, C.[Chengru],
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS,
CVPR23(11940-11949)
IEEE DOI 2309
BibRef

Xie, B.[Beini], Chang, H.[Heng], Zhang, Z.W.[Zi-Wei], Wang, X.[Xin], Wang, D.[Daixin], Zhang, Z.Q.[Zhi-Qiang], Ying, R.[Rex], Zhu, W.W.[Wen-Wu],
Adversarially Robust Neural Architecture Search for Graph Neural Networks,
CVPR23(8143-8152)
IEEE DOI 2309
BibRef

Zhang, X.Y.[Xuan-Yang], Li, Y.G.[Yong-Gang], Zhang, X.Y.[Xiang-Yu], Wang, Y.T.[Yong-Tao], Sun, J.[Jian],
Differentiable Architecture Search with Random Features,
CVPR23(16060-16069)
IEEE DOI 2309
BibRef

Yamada, R.[Ryosuke], Shinoda, R.[Risa], Kataoka, H.[Hirokatsu],
Exploring the Potential of Neural Dataset Search,
NAS23(2259-2266)
IEEE DOI 2309
BibRef

Hendrickx, L.[Lotte], Symons, A.[Arne], van Ranst, W.[Wiebe], Verhelst, M.[Marian], Goedemé, T.[Toon],
Hardware-aware NAS by Genetic Optimisation with a Design Space Exploration Simulator,
NAS23(2275-2283)
IEEE DOI 2309
BibRef

Liao, P.[Peng], Jin, Y.C.[Yao-Chu], Du, W.L.[Wen-Li],
EMT-NAS: Transferring architectural knowledge between tasks from different datasets,
CVPR23(3643-3653)
IEEE DOI 2309
BibRef

Zhao, P.[Ping], Chen, P.Y.[Pan-Yue], Liu, G.M.[Guan-Ming],
Training-free NAS for 3d Point Cloud Processing,
ACCV22(I:296-310).
Springer DOI 2307
BibRef

Li, Z.W.[Zhuo-Wei], Gao, Y.[Yibo], Zha, Z.Z.[Zhen-Zhou], Hu, Z.Q.[Zhi-Qiang], Xia, Q.[Qing], Zhang, S.T.[Shao-Ting], Metaxas, D.N.[Dimitris N.],
Towards Self-supervised and Weight-preserving Neural Architecture Search,
SelfLearn22(3-19).
Springer DOI 2304
BibRef

Li, Y.H.[Yun-Hong], Li, S.[Shuai], Yu, Z.H.[Zhen-Hua],
DARTS-PAP: Differentiable Neural Architecture Search by Polarization of Instance Complexity Weighted Architecture Parameters,
MMMod23(II: 277-288).
Springer DOI 2304
BibRef

Yang, T.[Taojiannan], Yang, L.J.[Lin-Jie], Jin, X.J.[Xiao-Jie], Chen, C.[Chen],
Revisiting Training-free NAS Metrics: An Efficient Training-based Method,
WACV23(4740-4749)
IEEE DOI 2302
Measurement, Costs, Systematics, Correlation, Error analysis, Graphics processing units, visual reasoning BibRef

Cavagnero, N.[Niccolò], Robbiano, L.[Luca], Caputo, B.[Barbara], Averta, G.[Giuseppe],
FreeREA: Training-Free Evolution-based Architecture Search,
WACV23(1493-1502)
IEEE DOI 2302
Training, Measurement, Costs, Computational modeling, Search methods, Neural networks, Memory management, visual reasoning BibRef

Vu, T.[Thanh], Zhou, Y.Q.[Yan-Qi], Wen, C.F.[Chun-Feng], Li, Y.[Yueqi], Frahm, J.M.[Jan-Michael],
Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search,
WACV23(1400-1410)
IEEE DOI 2302
Training, Transfer learning, Benchmark testing, Multitasking, Boosting, Algorithms: Machine learning architectures, Embedded sensing/real-time techniques BibRef

Yu, Z.W.[Zhe-Wen], Bouganis, C.S.[Christos-Savvas],
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search,
WACV23(1503-1512)
IEEE DOI 2302
Degradation, Deep learning, Couplings, Neural networks, Space exploration, Algorithms: Machine learning architectures, and algorithms (including transfer) BibRef

Das, M.[Mayukh], Singh, B.[Brijraj], Chheda, H.K.[Harsh K.], Sharma, P.[Pawan], NS, P.[Pradeep],
AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement,
ICPR22(2568-2574)
IEEE DOI 2212
Training, Power demand, Production, Search problems, Hardware, Behavioral sciences BibRef

Hu, Y.[Yue], Shen, C.[Chongfei], Yang, L.X.[Li-Xin], Wu, Z.P.[Zhi-Peng], Liu, Y.[Yu],
A Novel Predictor with Optimized Sampling Method for Hardware-aware NAS,
ICPR22(2114-2120)
IEEE DOI 2212
Training, Semiconductor device measurement, Neural networks, Network architecture, Sampling methods, Hardware BibRef

Nguyen, X.S.[Xuan Son],
A Gyrovector Space Approach for Symmetric Positive Semi-definite Matrix Learning,
ECCV22(XXVII:52-68).
Springer DOI 2211
BibRef

Liu, Z.[Zechun], Shen, Z.Q.[Zhi-Qiang], Long, Y.[Yun], Xing, E.[Eric], Cheng, K.T.[Kwang-Ting], Leichner, C.[Chas],
Data-Free Neural Architecture Search via Recursive Label Calibration,
ECCV22(XXIV:391-406).
Springer DOI 2211
BibRef

Wang, Q.[Qiang], Shi, S.[Shaohuai], Zhao, K.[Kaiyong], Chu, X.W.[Xiao-Wen],
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching,
ECCV22(XXXII:437-453).
Springer DOI 2211
BibRef

He, W.[Wei], Yao, Q.M.[Quan-Ming], Yokoya, N.[Naoto], Uezato, T.[Tatsumi], Zhang, H.Y.[Hong-Yan], Zhang, L.P.[Liang-Pei],
Spectrum-Aware and Transferable Architecture Search for Hyperspectral Image Restoration,
ECCV22(XIX:19-37).
Springer DOI 2211
BibRef

Lukasik, J.[Jovita], Jung, S.[Steffen], Keuper, M.[Margret],
Learning Where to Look: Generative NAS is Surprisingly Efficient,
ECCV22(XXIII:257-273).
Springer DOI 2211
BibRef

Qian, Y.G.[Ya-Guan], Huang, S.H.[Sheng-Hui], Wang, B.[Bin], Ling, X.[Xiang], Guan, X.H.[Xiao-Hui], Gu, Z.Q.[Zhao-Quan], Zeng, S.N.[Shao-Ning], Zhou, W.[Wujie], Wang, H.J.[Hai-Jiang],
Robust Network Architecture Search via Feature Distortion Restraining,
ECCV22(V:122-138).
Springer DOI 2211
BibRef

You, H.R.[Hao-Ran], Li, B.[Baopu], Sun, Z.Y.[Zhan-Yi], Ouyang, X.[Xu], Lin, Y.Y.[Ying-Yan],
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning,
ECCV22(XI:674-690).
Springer DOI 2211
BibRef

Yüzügüler, A.C.[Ahmet Caner], Dimitriadis, N.[Nikolaos], Frossard, P.[Pascal],
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search,
ECCV22(XII:173-190).
Springer DOI 2211
BibRef

Cai, H.[He], Zhang, Z.[Zhaokai], Feng, T.P.[Tian-Peng], Guo, Y.D.[Yan-Dong],
DARTS-PD: Differentiable Architecture Search with Path-Wise Weight Sharing Derivation,
ICIP22(1256-1260)
IEEE DOI 2211
Search methods, Artificial neural networks, Optimization, Neural Architecture Search, path-wise weight sharing derivation BibRef

Pourchot, A.[Aloïs], Bailly, K.[Kévin], Ducarouge, A.[Alexis], Sigaud, O.[Olivier],
Neural Architecture Search for Fracture Classification,
ICIP22(3226-3230)
IEEE DOI 2211
Protocols, Computational modeling, Transfer learning, Search problems, Computational efficiency, Fracture Classification BibRef

Ying, G.H.[Guo-Hao], He, X.[Xin], Gao, B.[Bin], Han, B.[Bo], Chu, X.W.[Xiao-Wen],
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs,
ECCV22(XVI:37-53).
Springer DOI 2211
BibRef

Xue, C.[Chao], Wang, X.X.[Xiao-Xing], Yan, J.C.[Jun-Chi], Li, C.G.[Chun-Guang],
A Max-Flow Based Approach for Neural Architecture Search,
ECCV22(XX:685-701).
Springer DOI 2211
BibRef

Liu, J.[Jihao], Huang, X.[Xin], Song, G.[Guanglu], Li, H.S.[Hong-Sheng], Liu, Y.[Yu],
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP,
ECCV22(XXI:33-49).
Springer DOI 2211
BibRef

Su, X.[Xiu], You, S.[Shan], Xie, J.[Jiyang], Zheng, M.[Mingkai], Wang, F.[Fei], Qian, C.[Chen], Zhang, C.S.[Chang-Shui], Wang, X.G.[Xiao-Gang], Xu, C.[Chang],
ViTAS: Vision Transformer Architecture Search,
ECCV22(XXI:139-157).
Springer DOI 2211
BibRef

Liu, C.X.[Chen-Xi], Leng, Z.Q.[Zhao-Qi], Sun, P.[Pei], Cheng, S.Y.[Shu-Yang], Qi, C.R.[Charles R.], Zhou, Y.[Yin], Tan, M.X.[Ming-Xing], Anguelov, D.[Dragomir],
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds,
ECCV22(XXI:158-175).
Springer DOI 2211
BibRef

Zhang, M.[Miao], Pan, S.R.[Shi-Rui], Chang, X.J.[Xiao-Jun], Su, S.[Steven], Hu, J.L.[Ji-Lin], Haffari, G.[Gholamreza], Yang, B.[Bin],
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule,
CVPR22(11861-11870)
IEEE DOI 2210
Deep learning, Costs, Memory management, Optimization methods, Benchmark testing, Gaussian distribution, Optimization methods BibRef

Xiao, H.[Han], Wang, Z.W.[Zi-Wei], Zhu, Z.[Zheng], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search,
CVPR22(11882-11891)
IEEE DOI 2210
Costs, Monte Carlo methods, Fluctuations, Codes, Approximation algorithms, Deep learning architectures and techniques BibRef

Huang, T.[Tao], You, S.[Shan], Wang, F.[Fei], Qian, C.[Chen], Zhang, C.S.[Chang-Shui], Wang, X.G.[Xiao-Gang], Xu, C.[Chang],
GreedyNASv2: Greedier Search with a Greedy Path Filter,
CVPR22(11892-11901)
IEEE DOI 2210
Training, Reliability, Deep learning architectures and techniques, retrieval BibRef

Zhou, Q.Q.[Qin-Qin], Sheng, K.[Kekai], Zheng, X.[Xiawu], Li, K.[Ke], Sun, X.[Xing], Tian, Y.H.[Yong-Hong], Chen, J.[Jie], Ji, R.R.[Rong-Rong],
Training-free Transformer Architecture Search,
CVPR22(10884-10893)
IEEE DOI 2210
Graphics processing units, Transformers, Task analysis, Explainable computer vision BibRef

Ye, P.[Peng], Li, B.[Baopu], Li, Y.[Yikang], Chen, T.[Tao], Fan, J.Y.[Jia-Yuan], Ouyang, W.L.[Wan-Li],
beta-DARTS: Beta-Decay Regularization for Differentiable Architecture Search,
CVPR22(10864-10873)
IEEE DOI 2210
Training, Deep learning, Costs, Neural networks, Search problems, retrieval BibRef

Hendrickx, L.[Lotte], van Ranst, W.[Wiebe], Goedemé, T.[Toon],
Hot-started NAS for Task-specific Embedded Applications,
NAS22(1970-1977)
IEEE DOI 2210
Knowledge engineering, Neural networks, Size measurement, Search problems BibRef

Moser, B.[Brian], Raue, F.[Federico], Hees, J.[Jörn], Dengel, A.[Andreas],
Less is More: Proxy Datasets in NAS approaches,
NAS22(1952-1960)
IEEE DOI 2210
Training, Neural networks, Training data, Search problems BibRef

Li, W.S.[Wen-Shuo], Chen, X.H.[Xing-Hao], Bai, J.Y.[Jin-Yu], Ning, X.F.[Xue-Fei], Wang, Y.H.[Yun-He],
Searching for Energy-Efficient Hybrid Adder-Convolution Neural Networks,
NAS22(1942-1951)
IEEE DOI 2210
Training, Energy consumption, Convolution, Computational modeling, Neural networks, Computer architecture BibRef

Geada, R.[Rob], McGough, A.S.[Andrew Stephen],
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics,
NAS22(1961-1969)
IEEE DOI 2210
Measurement, Runtime, Heuristic algorithms, Microprocessors, Neural networks, Manuals BibRef

Ding, Y.D.[Ya-Dong], Wu, Y.[Yu], Huang, C.Y.[Cheng-Yue], Tang, S.L.[Si-Liang], Yang, Y.[Yi], Wei, L.[Longhui], Zhuang, Y.T.[Yue-Ting], Tian, Q.[Qi],
Learning to Learn by Jointly Optimizing Neural Architecture and Weights,
CVPR22(129-138)
IEEE DOI 2210
Training, Backpropagation, Adaptation models, Computational efficiency, Self- semi- meta- unsupervised learning BibRef

Arican, M.E.[Metin Ersin], Kara, O.[Ozgur], Bredell, G.[Gustav], Konukoglu, E.[Ender],
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior,
CVPR22(1950-1958)
IEEE DOI 2210
Training, Computational modeling, Superresolution, Image restoration, Self- semi- meta- unsupervised learning BibRef

Wang, H.X.[Hao-Xiang], Wang, Y.[Yite], Sun, R.[Ruoyu], Li, B.[Bo],
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning,
CVPR22(9787-9798)
IEEE DOI 2210
Deep learning, Costs, Neural networks, Supervised learning, Kernel, Self- semi- meta- Transfer/low-shot/long-tail learning BibRef

Pan, J.[Junyi], Sun, C.[Chong], Zhou, Y.Z.[Yi-Zhou], Zhang, Y.[Ying], Li, C.[Chen],
Distribution Consistent Neural Architecture Search,
CVPR22(10874-10883)
IEEE DOI 2210
Training, Couplings, Weight measurement, Computational modeling, Benchmark testing, Search problems, retrieval BibRef

Mok, J.[Jisoo], Na, B.[Byunggook], Kim, J.H.[Ji-Hoon], Han, D.Y.[Dong-Yoon], Yoon, S.[Sungroh],
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?,
CVPR22(11851-11860)
IEEE DOI 2210
Training, Measurement, Costs, Correlation, Deep learning architectures and techniques BibRef

Huang, M.B.[Min-Bin], Huang, Z.J.[Zhi-Jian], Li, C.L.[Chang-Lin], Chen, X.[Xin], Xu, H.[Hang], Li, Z.G.[Zhen-Guo], Liang, X.D.[Xiao-Dan],
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search,
CVPR22(11871-11881)
IEEE DOI 2210
Knowledge engineering, Correlation, Predictive models, Prediction algorithms, Multitasking, Transfer/low-shot/long-tail learning BibRef

Zheng, X.[Xiawu], Fei, X.[Xiang], Zhang, L.[Lei], Wu, C.L.[Cheng-Lin], Chao, F.[Fei], Liu, J.Z.[Jian-Zhuang], Zeng, W.[Wei], Tian, Y.H.[Yong-Hong], Ji, R.R.[Rong-Rong],
Neural Architecture Search with Representation Mutual Information,
CVPR22(11902-11911)
IEEE DOI 2210
Training, Performance evaluation, Deep learning, Architecture, Estimation, Efficient learning and inferences BibRef

Peng, C.[Cheng], Myronenko, A.[Andriy], Hatamizadeh, A.[Ali], Nath, V.[Vishwesh], Siddiquee, M.M.R.[Md Mahfuzur Rahman], He, Y.F.[Yu-Fan], Xu, D.[Daguang], Chellappa, R.[Rama], Yang, D.[Dong],
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet,
CVPR22(20709-20719)
IEEE DOI 2210
Training, Image segmentation, Shape, Network architecture, Topology, Medical, grouping and shape analysis BibRef

Xu, K.[Kepeng], He, G.[Gang],
DNAS:A Decoupled Global Neural Architecture Search Method,
NAS22(1978-1984)
IEEE DOI 2210
Analytical models, Search methods, Benchmark testing BibRef

Akin, B.[Berkin], Gupta, S.[Suyog], Long, Y.[Yun], Spiridonov, A.[Anton], Wang, Z.[Zhuo], White, M.[Marie], Xu, H.[Hao], Zhou, P.[Ping], Zhou, Y.Q.[Yan-Qi],
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs,
ECV22(2666-2675)
IEEE DOI 2210
Tensors, Costs, Convolution, Image edge detection, Throughput BibRef

Qian, G.[Guocheng], Zhang, X.[Xuanyang], Li, G.H.[Guo-Hao], Zhao, C.[Chen], Chen, Y.[Yukang], Zhang, X.Y.[Xiang-Yu], Ghanem, B.[Bernard], Sun, J.[Jian],
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search,
ECV22(2781-2786)
IEEE DOI 2210
Costs, Codes, Graphics processing units, Pattern recognition BibRef

Chen, Z.[Ziye], Zhan, Y.B.[Yi-Bing], Yu, B.[Baosheng], Gong, M.M.[Ming-Ming], Du, B.[Bo],
Not All Operations Contribute Equally: Hierarchical Operation-adaptive Predictor for Neural Architecture Search,
ICCV21(10488-10497)
IEEE DOI 2203
Microprocessors, Logic gates, Representation learning, Recognition and classification 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, Prediction algorithms, Computational efficiency, 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, Machine learning architectures and formulations BibRef

Ci, Y.Z.[Yuan-Zheng], Lin, C.[Chen], Sun, M.[Ming], Chen, B.[Boyu], Zhang, H.W.[Hong-Wen], Ouyang, W.L.[Wan-Li],
Evolving Search Space for Neural Architecture Search,
ICCV21(6639-6649)
IEEE DOI 2203
Codes, Automation, Extraterrestrial phenomena, Performance gain, Search problems, BibRef

Chu, X.X.[Xiang-Xiang], Zhang, B.[Bo], Xu, R.J.[Rui-Jun],
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search,
ICCV21(12219-12228)
IEEE DOI 2203
Training, Computational modeling, Pipelines, Graphics processing units, 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, grouping and shape BibRef

Wang, Y.M.[Yao-Ming], Liu, Y.C.[Yu-Chen], Dai, W.R.[Wen-Rui], Li, C.L.[Cheng-Lin], 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, 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, 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, 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, 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, Network architecture, Convergence, Recognition and classification, 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, Interference, Manuals, Machine learning architectures and formulations 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, Manuals, Games, Statistical Methods, Learning and Optimization Deep Learning BibRef

Wan, X.C.[Xing-Chen], 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, 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, Search problems, Hardware, Deep Learning Deep Learning -> Efficient Training and Inference Methods for Networks 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, Mixed Precision 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, 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, 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, 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, Stability analysis 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

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, Streaming media, Real-time systems 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, 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, 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, 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, Transformers, Search problems, Encoding 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, 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, 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, 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, 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

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, Search problems BibRef

Liu, H.X.[Han-Xiao], Simonyan, K.[Karen], Yang, Y.M.[Yi-Ming],
DARTS: Differentiable architecture search,
ICLR19
WWW Link. BibRef 1900

Zhang, X.Y.[Xuan-Yang], 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, Task analysis BibRef

Yang, Z.H.[Zhao-Hui], Wang, Y.H.[Yun-He], Chen, X.H.[Xing-Hao], Guo, J.Y.[Jian-Yuan], Zhang, W.[Wei], Xu, C.[Chao], Xu, C.J.[Chun-Jing], 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, Complexity theory 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, Network architecture 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, 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 BibRef

Duan, Y.W.[Ya-Wen], 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, 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, Debugging, Extraterrestrial measurements BibRef

Chu, X.X.[Xiang-Xiang], Zhang, B.[Bo], Ma, H.L.[Hai-Long], Xu, R.J.[Rui-Jun], 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, Reinforcement learning BibRef

Zhang, H.G.[Hui-Gang], 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, Search problems 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, 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, 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, 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

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, 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, 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

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.J.[Shao-Ju], 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

Howard-Jenkins, H.[Henry], Li, Y.W.[Yi-Wen], 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.D.[Yu-Ding], Guo, Z.C.[Zi-Chao], Wan, R.[Ruosi], Zhang, X.Y.[Xiang-Yu], Wei, Y.C.[Yi-Chen], 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.H.[Zi-Hao], 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

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
Search problems, DNA, Linear programming, Task analysis, Estimation, Loss measurement 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, Task analysis, Neural networks, Hardware, Measurement 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
Search problems, Google, Inference algorithms, Task analysis, Training 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, 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, 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, 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, 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
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, 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
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, Optimization, Training, Search problems, Measurement, Machine learning BibRef

Atzmon, M.[Matan], Nagano, K.[Koki], Fidler, S.[Sanja], Khamis, S.[Sameh], Lipman, Y.[Yaron],
Frame Averaging for Equivariant Shape Space Learning,
CVPR22(621-631)
IEEE DOI 2210
Training, Representation learning, Shape, Neural networks, Decoding, Representation learning 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
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, Optimization methods, Training data, Neural networks 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
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. 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

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

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

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

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

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, 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 .