14.5.10.3 Neural Architecture, Neural Architecture Search

Banarse, D.S., Duller, A.W.G.,
Deformation Invariant Visual Object Recognition: Experiments with a Self-Organizing Neural Architecture,
NeurCompApp(6), No. 2, 1997, pp. 79-90. 9801
BibRef

Shih, F.Y.[Frank Y.], Moh, J.[Jenlong], Chang, F.C.[Fu-Chun],
A new art-based neural architecture for pattern classification and image enhancement without prior knowledge,
PR(25), No. 5, May 1992, pp. 533-542.
Elsevier DOI 0401
BibRef

Taylor, J.G., Hartley, M., Taylor, N., Panchev, C., Kasderidis, S.,
A hierarchical attention-based neural network architecture, based on human brain guidance, for perception, conceptualisation, action and reasoning,
IVC(27), No. 11, 2 October 2009, pp. 1641-1657.
Elsevier DOI 0909
Dorsal and ventral vision; Object representations; Dopamine as reward; TD learning BibRef

Lerouge, J., Herault, R., Chatelain, C., Jardin, F., Modzelewski, R.,
IODA: An input/output deep architecture for image labeling,
PR(48), No. 9, 2015, pp. 2847-2858.
Elsevier DOI 1506
Deep learning architectures BibRef

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

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

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

Dong, H., Zou, B., Zhang, L., Zhang, S.,
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification,
GeoRS(58), No. 9, September 2020, pp. 6362-6375.
IEEE DOI 2008
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

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,
IJCV(130), No. 2, February 2022, pp. 201-225.
Springer DOI 2202
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

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 density criterion,
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): A novel neural architecture for sparse coding and beyond,
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, 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.[Yiwen], 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

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
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
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

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
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.[Jinyuan], Wu, Y.[Yuhui], Wu, G.[Guanyao], Liu, R.[Risheng], 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

Guo, Y.[Yong], Zheng, Y.[Yin], Tan, M.K.[Ming-Kui], Chen, Q.[Qi], Li, Z.P.[Zhi-Peng], Chen, J.[Jian], Zhao, P.[Peilin], Huang, J.Z.[Jun-Zhou],
Towards Accurate and Compact Architectures via Neural Architecture Transformer,
PAMI(44), No. 10, October 2022, pp. 6501-6516.
IEEE DOI 2209
Optimization, Computational efficiency, Convolution, Computational modeling, Kernel, Microprocessors, operation transition BibRef

Shen, H.[Hao], Zhao, Z.Q.[Zhong-Qiu], Liao, W.[Wenrui], 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

Cao, H.M.[Hui-Min], Luo, X.B.[Xiao-Bo], Peng, Y.D.[Yi-Dong], Xie, T.S.[Tian-Shou],
MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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

Li, H.F.[Hai-Feng], Xu, C.[Cong], Ma, L.[Lin], Bo, H.J.[Hong-Jian], Zhang, D.[David],
MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion,
PAMI(44), No. 12, December 2022, pp. 9417-9433.
IEEE DOI 2212
Neurons, Biological neural networks, Task analysis, Brain modeling, Parallel processing, Information processing, Training, parallel computing 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.[Xiuwei], Cui, H.[Hengfei], 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

Tayanov, V.[Vitaliy], Krzyzak, A.[Adam], Suen, C.Y.[Ching Y.],
Analysis of Different Deep Learning Architectures to Learn Generalised Classifier Stacking on Riemannian and Grassmann Manifolds,
ICPR22(2735-2741)
IEEE DOI 2212
Manifolds, Geometry, Architecture, Stacking, Deep architecture, Predictive models 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.[Quanming], 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

Prach, B.[Bernd], Lampert, C.H.[Christoph H.],
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks,
ECCV22(XXI:350-365).
Springer DOI 2211

WWW Link. BibRef

Zhao, Y.[Yue], Chen, J.Z.[Jun-Zhou], Zhang, Z.[Zirui], Zhang, R.H.[Rong-Hui],
BA-Net: Bridge Attention for Deep Convolutional Neural Networks,
ECCV22(XXI:297-312).
Springer DOI 2211

WWW Link. BibRef

Davis, J.[Jim], Frank, L.[Logan],
Revisiting Batch Norm Initialization,
ECCV22(XXI:212-228).
Springer DOI 2211

WWW Link. BibRef

Trimmel, M.[Martin], Zanfir, M.[Mihai], Hartley, R.I.[Richard I.], Sminchisescu, C.[Cristian],
ERA: Enhanced Rational Activations,
ECCV22(XX:722-738).
Springer DOI 2211
ReLU BibRef

Yuan, W.T.[Wen-Tao], Zhu, Q.T.[Qing-Tian], Liu, X.Y.[Xiang-Yue], Ding, Y.K.[Yi-Kang], Zhang, H.T.[Hao-Tian], Zhang, C.[Chi],
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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,
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Cai, H.[He], Zhang, Z.[Zhaokai], Feng, T.[Tianpeng], 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,
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Wang, X.X.[Xiao-Xing], Lin, J.[Jiale], Zhao, J.[Juanping], Yang, X.K.[Xiao-Kang], Yan, J.C.[Jun-Chi],
EAutoDet: Efficient Architecture Search for Object Detection,
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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,
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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
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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
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Liu, C.X.[Chen-Xi], Leng, Z.Q.[Zhao-Qi], Sun, P.[Pei], Cheng, S.[Shuyang], 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,
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Utasi, Á.[Ákos],
PEA: Improving the Performance of ReLU Networks for Free by Using Progressive Ensemble Activations,
ECV22(2797-2805)
IEEE DOI 2210
Training, Image segmentation, Semantics, Neural networks, Network architecture 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.[Ziwei], 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, Pattern recognition, Reliability, Deep learning architectures and techniques, retrieval BibRef

Ding, X.H.[Xiao-Han], Zhang, X.Y.[Xiang-Yu], Han, J.G.[Jun-Gong], Ding, G.G.[Gui-Guang],
Scaling Up Your Kernels to 31X31: Revisiting Large Kernel Design in CNNs,
CVPR22(11953-11965)
IEEE DOI 2210
Convolutional codes, Shape, Scalability, Transformers, Data models, Pattern recognition, Convolutional neural networks, Deep learning architectures and techniques BibRef

Lin, F.Q.[Fan-Qing], Price, B.[Brian], Martinez, T.[Tony],
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks,
CVPR22(763-772)
IEEE DOI 2210
Deep learning, Image segmentation, Visualization, Shape, Semantics, Estimation, Deep learning architectures and techniques, Vision applications and systems 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, Pattern recognition, 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, Pattern recognition 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

Liu, C.J.[Chuan-Jian], Han, K.[Kai], Xiao, A.[An], Nie, Y.[Ying], Zhang, W.[Wei], Wang, Y.H.[Yun-He],
Network Amplification with Efficient MACs Allocation,
NAS22(1932-1941)
IEEE DOI 2210
Statistical analysis, Computational modeling, Heuristic algorithms, Neural networks, Network architecture, Dynamic programming 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, Pattern recognition, 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, Pattern recognition, 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, Pattern recognition, 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.[Chenglin], 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, Pattern recognition, Efficient learning and inferences BibRef

Liu, S.[Shuang], Okatani, T.[Takayuki],
Symmetry-aware Neural Architecture for Embodied Visual Exploration,
CVPR22(17221-17230)
IEEE DOI 2210
Training, Visualization, Simultaneous localization and mapping, Convolution, Neural networks, Training data, Robot vision 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, Pattern recognition 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

Courtois, A.[Adrien], Morel, J.M.[Jean-Michel], Arias, P.[Pablo],
Investigating Neural Architectures by Synthetic Dataset Design,
VDU22(4886-4895)
IEEE DOI 2210
Systematics, Neural networks, Buildings, Estimation, Computer architecture 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

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, 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, 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, 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, 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

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, Transforms, 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

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, 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, 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, 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, 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, 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, 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, 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, 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

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, 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, 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, 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

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, 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, 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

Li, Y.[Yawei], Li, W.[Wen], Danelljan, M.[Martin], Zhang, K.[Kai], Gu, S.H.[Shu-Hang], 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, 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, 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, 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, Pattern recognition, 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, 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, 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.K.[Ming-Kui],
Contrastive Neural Architecture Search with Neural Architecture Comparators,
CVPR21(9497-9506)
IEEE DOI 2111
Training data, 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, 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, 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, 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

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, 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 uncertainty 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, 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, 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, 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, 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

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

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
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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
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Chen, X.[Xin], Duan, Y.W.[Ya-Wen], Chen, Z.W.[Ze-Wei], Xu, H.[Hang], Chen, Z.[Zihao], Liang, X.D.[Xiao-Dan], Zhang, T.[Tong], Li, Z.G.[Zhen-Guo],
Catch: Context-based Meta Reinforcement Learning for Transferrable 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
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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

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, 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, 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
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, 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, Pattern recognition, Representation 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
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

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
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

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.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

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 BibRef

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

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

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

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

Yang, Z.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 .