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Differentiable architecture search (DARTS) is a prevailing NAS solution to identify architectures.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Evolving neural networks through augmenting topologies
Stanley, K. O. and Miikkulainen, R · 2002
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 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
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Accelerating neural architecture search using performance prediction, 2017
Baker, B., Gupta, O., Raskar, R., and Naik, N · 2017
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Improved regularization of convolutional neural networks with cutout, 2017
DeVries, T. and Taylor, G. W · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Maaten, L. v. d., and Weinberger, K. Q · 2017
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Learning curve prediction with bayesian neural networks
Klein, A., Falkner, S., Springenberg, J. T., and Hutter, F · 2017
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Hierarchical representations for efficient architecture search, 2017
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Tan, J., Le, Q. V., and Kurakin, A · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., and Le, Q · 2018
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SMASH: One-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J., and Weston, N · 2018
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Neural architecture optimization
Luo, R., Tian, F., Qin, T., Chen, E., and Liu, T.-Y · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J · 2018
Cited alongside, same era.
Breaking the softmax bottleneck: A high-rank RNN language model
Yang, Z., Dai, Z., Salakhutdinov, R., and Cohen, W. W · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
Zhang, X., Zhou, X., Lin, M., and Sun, J · 2018
Cited alongside, same era.
Practical block-wise neural network architecture generation
Zhong, Z., Yan, J., Wu, W., Shao, J., and Liu, C.-L · 2018
DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
Later among the works it cites.
Rob-gan: Generator, discriminator, and adversarial attacker
Liu, X. and Hsieh, C.-J · 2019
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Evolving deep neural networks
Miikkulainen, R., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., and et al · 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
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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
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SNAS: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2019
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Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Cited alongside, same era.
ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
Cited alongside, same era.
Progressive differentiable architecture search: Bridging the depth gap between search and evaluation, 2019
Chen, X., Xie, L., Wu, J., and Tian, Q · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Dong, X. and Yang, Y · 2019
Cited alongside, same era.
Efficient multi-objective neural architecture search via lamarckian evolution
Elsken, T., Metzen, J. H., and Hutter, F · 2019
Cited alongside, same era.
NAS-bench-101: Towards reproducible neural architecture search
Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., and Hutter, F · 2019
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Bayesnas: A bayesian approach for neural architecture search
Zhou, H., Yang, M., Wang, J., and Pan, W · 2019
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Milenas: Efficient neural architecture search via mixed-level reformulation, 2020
He, C., Ye, H., Shen, L., and Zhang, T · 2020
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How does noise help robustness? explanation and exploration under the neural sde framework
Liu, X., Xiao, T., Si, S., Cao, Q., Kumar, S., and Hsieh, C.-J · 2020
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Understanding architectures learnt by cell-based neural architecture search
Shu, Y., Wang, W., and Cai, S · 2020
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Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A. L., and Le, Q. V · 2020
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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 · 2020
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Efficient neural interaction function search for collaborative filtering
Yao, Q., Chen, X., Kwok, J. T., Li, Y., and Hsieh, C.-J · 2020
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Evaluating the search phase of neural architecture search
Yu, K., Sciuto, C., Jaggi, M., Musat, C., and Salzmann, M · 2020
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