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Neural Architecture Search (NAS) automates and prospers the design of neural networks.
Darts+: Improved differentiable architecture search with early stopping
Liang, H.; Zhang, S.; Sun, J.; He, X.; Huang, W.; Zhuang, K.; and Li, Z. 2019 · 1909
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Neural predictor for neural architecture search
Wen, W.; Liu, H.; Li, H.; Chen, Y.; Bender, G.; and Kindermans, P.-J. 2019 · 1912
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Estimates of the regression coefficient based on Kendall’s tau
Sen, P. K. 1968 · 1968
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Autoencoders, minimum description length and Helmholtz free energy
Hinton, G. E.; and Zemel, R. S. 1994 · 1994
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Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T.; and Saul, L. K. 2000 · 2000
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Exactly Computing the Local Lipschitz Constant of ReLU Networks
Jordan, M.; and Dimakis, A. G. 2020 · 2003
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A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Ning, X.; Zheng, Y.; Zhao, T.; Wang, Y.; and Yang, H. 2020 · 2004
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Pearson correlation coefficient
Benesty, J.; Chen, J.; Huang, Y.; and Cohen, I. 2009 · 2009
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Weisfeiler-lehman graph kernels
Shervashidze, N.; Schweitzer, P.; Leeuwen, E. J. v.; Mehlhorn, K.; and Borgwardt, K. M. 2011 · 2011
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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DeepWalk: Online Learning of Social Representations
Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014 · 2014
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Analyzing noise in autoencoders and deep networks
Poole, B.; Sohl-Dickstein, J.; and Ganguli, S. 2014 · 2014
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Going deeper with convolutions
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; and Rabinovich, A. 2015 · 2015
Earlier work this paper cites.
Deep neural networks for learning graph representations
Cao, S.; Lu, W.; and Xu, Q. 2016 · 2016
Cited alongside, same era.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A.; and Leskovec, J. 2016 · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Structural deep network embedding
Wang, D.; Cui, P.; and Zhu, W. 2016 · 2016
Cited alongside, same era.
Accelerating neural architecture search using performance prediction
Baker, B.; Gupta, O.; Raskar, R.; and Naik, N. 2017 · 2017
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Chen, X.; Xie, L.; Wu, J.; and Tian, Q. 2019 · 2019
Later among the works it cites.
Searching for a robust neural architecture in four gpu hours
Dong, X.; and Yang, Y. 2019 · 2019
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DARTS: Differentiable architecture search
Liu, H.; Simonyan, K.; and Yang, Y. 2019 · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Real, E.; Aggarwal, A.; Huang, Y.; and Le, Q. V. 2019 · 2019
Later among the works it cites.
Mnasnet: Platform-aware neural architecture search for mobile
Tan, M.; Chen, B.; Pang, R.; Vasudevan, V.; Sandler, M.; Howard, A.; and Le, Q. V. 2019 · 2019
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PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
Xu, Y.; Xie, L.; Zhang, X.; Chen, X.; Qi, G.-J.; Tian, Q.; and Xiong, H. 2019 · 2019
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Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
Cited alongside, same era.
Graph embedding techniques, applications, and performance: A survey
Goyal, P.; and Ferrara, E. 2018 · 2018
Cited alongside, same era.
Neural architecture optimization
Luo, R.; Tian, F.; Qin, T.; Chen, E.; and Liu, T.-Y. 2018 · 2018
Cited alongside, same era.
Efficient Neural Architecture Search via Parameter Sharing
Pham, H.; Guan, M.; Zoph, B.; Le, Q.; and Dean, J. 2018 · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
Cited alongside, same era.
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A.; and Scaman, K. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
NAS-Bench-101: Towards Reproducible Neural Architecture Search
Ying, C.; Klein, A.; Christiansen, E.; Real, E.; Murphy, K.; and Hutter, F. 2019 · 2019
Later among the works it cites.
D-vae: A variational autoencoder for directed acyclic graphs
Zhang, M.; Jiang, S.; Cui, Z.; Garnett, R.; and Chen, Y. 2019 · 2019
Later among the works it cites.
BayesNAS: A Bayesian Approach for Neural Architecture Search
Zhou, H.; Yang, M.; Wang, J.; and Pan, W. 2019 · 2019
Later among the works it cites.
Milenas: Efficient neural architecture search via mixed-level reformulation
He, C.; Ye, H.; Shen, L.; and Zhang, T. 2020 · 2020
Closest in time.
Neural Graph Embedding for Neural Architecture Search
Li, W.; Gong, S.; and Xiatian, Z. 2020 · 2020
Closest in time.
GP-NAS: Gaussian Process Based Neural Architecture Search
Li, Z.; Xi, T.; Deng, J.; Zhang, G.; Wen, S.; and He, R. 2020 · 2020
Closest in time.
Neural architecture search with bayesian optimisation and optimal transport
Kandasamy, K.; Neiswanger, W.; Schneider, J.; Poczos, B.; and Xing, E. P. 2018 · 2025
Closest in time.