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Neural architecture search (NAS), an important branch of automatic machine learning, has become an effective approach to automate the design of deep learning models.
Detnas: Neural architecture search on object detection
Chen, Y., Yang, T., Zhang, X., Meng, G., Pan, C., and Sun, J · 1903
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Optimal packing and covering in the plane are np-complete
Fowler, R. J., Paterson, M. S., and Tanimoto, S. L · 1981
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On the complexity of locating linear facilities in the plane
Megiddo, N. and Tamir, A · 1982
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Clustering to minimize the maximum intercluster distance
Gonzalez, T. F · 1985
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What’s wrong with mean-squared error?
Girod, B · 1993
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Image quality measures and their performance
Eskicioglu, A. M. and Fisher, P. S · 1995
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Geometric approximation via coresets
Agarwal, P. K., Har-Peled, S., Varadarajan, K. R., et al · 2005
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Core vector machines: Fast svm training on very large data sets
Tsang, I. W., Kwok, J. T., Cheung, P.-M., and Cristianini, N · 2005
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Simpler core vector machines with enclosing balls
Tsang, I. W., Kocsor, A., and Kwok, J. T · 2007
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Clarkson, K. L · 2010
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Facility location: concepts, models, algorithms and case studies. series: Contributions to management science: edited by zanjirani farahani, reza and hekmatfar, masoud, heidelberg, germany, physica-verlag, 2009, 549 pp., isbn 978-3-7908-2150-5 (hardprint), 978-3-7908-2151-2 (electronic), 2011
Wolf, G. W · 2011
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Using document summarization techniques for speech data subset selection
Wei, K., Liu, Y., Kirchhoff, K., and Bilmes, J · 2013
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Tiny imagenet visual recognition challenge
Le, Y. and Yang, X · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Coresets for scalable bayesian logistic regression
Huggins, J. H., Campbell, T., and Broderick, T · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
Cited alongside, same era.
Smash: one-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J. M., and Weston, N · 2017
Cited alongside, same era.
Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K · 2017
Nas-fpn: Learning scalable feature pyramid architecture for object detection
Ghiasi, G., Lin, T.-Y., and Le, Q. V · 2019
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Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A. L., and Fei-Fei, L · 2019
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Fast neural architecture search of compact semantic segmentation models via auxiliary cells
Nekrasov, V., Chen, H., Shen, C., and Reid, I · 2019
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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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Cited alongside, same era.
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
Cited alongside, same era.
Genetic cnn
Xie, L. and Yuille, A · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 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.
Faster coreset construction for projective clustering via low-rank approximation
Pratap, R. and Sen, S · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S · 2018
Cited alongside, same era.
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., and Keutzer, K · 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
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Evaluating the search phase of neural architecture search
Yu, K., Sciuto, C., Jaggi, M., Musat, C., and Salzmann, M · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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Da-nas: Data adapted pruning for efficient neural architecture search
Dai, X., Chen, D., Liu, M., Chen, Y., and Yuan, L · 2020
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Single path one-shot neural architecture search with uniform sampling
Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., and Sun, J · 2020
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Random search and reproducibility for neural architecture search
Li, L. and Talwalkar, A · 2020
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Small-gan: Speeding up gan training using core-sets
Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., and Odena, A · 2020
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Nas-fcos: Fast neural architecture search for object detection
Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., and Zhang, Y · 2020
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Efficient neural architecture search via proximal iterations
Yao, Q., Xu, J., Tu, W.-W., and Zhu, Z · 2020
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