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Designing feasible and effective architectures under diverse computation budgets incurred by different applications/devices is essential for deploying deep models in practice.
Backpropagation applied to handwritten zip code recognition
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An efficient boosting algorithm for combining preferences
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SPEA2+: improving the performance of the strength pareto evolutionary algorithm 2
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Learning to rank using gradient descent
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Imagenet classification with deep convolutional neural networks
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Large-scale evolution of image classifiers
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Neural architecture search with reinforcement learning
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Searching toward pareto-optimal device-aware neural architectures
Cheng, A.-C., Dong, J.-D., Hsu, C.-H., Chang, S.-H., Sun, M., Chang, S.-C., Pan, J.-Y., Chen, Y.-T., Wei, W., and Juan, D.-C · 2018
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Dpp-net: Device-aware progressive search for pareto-optimal neural architectures
Dong, J.-D., Cheng, A.-C., Juan, D.-C., Wei, W., and Sun, M · 2018
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Hierarchical representations for efficient architecture search
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., and Kavukcuoglu, K · 2018
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Efficient neural architecture search via parameter sharing
Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Single-path nas: Designing hardware-efficient convnets in less than 4 hours
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Efficientnet: Rethinking model scaling for convolutional neural networks
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Mnasnet: Platform-aware neural architecture search for mobile
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, J. H. M. and Hutter, F · 2019
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
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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ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
Chen, X., Xie, L., Wu, J., and Tian, Q · 2019
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NAT: Neural architecture transformer for accurate and compact architectures
Guo, Y., Zheng, Y., Tan, M., Chen, Q., Chen, J., Zhao, P., and Huang, J · 2019
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Searching for mobilenetv3
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al · 2019
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Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
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Nsga-net: neural architecture search using multi-objective genetic algorithm
Lu, Z., Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., and Banzhaf, W · 2019
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Xie, S., Kirillov, A., Girshick, R., and He, K · 2019
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Can weight sharing outperform random architecture search? an investigation with tunas
Bender, G., Liu, H., Chen, B., Chu, G., Cheng, S., Kindermans, P., and Le, Q. V · 2020
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Once for all: Train one network and specialize it for efficient deployment
Cai, H., Gan, C., and Han, S · 2020
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Ponas: Progressive one-shot neural architecture search for very efficient deployment
Huang, S.-Y. and Chu, W.-T · 2020
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Cream of the crop: Distilling prioritized paths for one-shot neural architecture search
Peng, H., Du, H., Yu, H., Li, Q., Liao, J., and Fu, J · 2020
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Neural architecture generator optimization
Ru, B., Esperanca, P., and Carlucci, F · 2020
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Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., Wu, B., Yu, M., Xu, T., Chen, K., et al · 2020
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Pc-darts: Partial channel connections for memory-efficient differentiable architecture search
Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.-J., Tian, Q., and Xiong, H · 2020
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