Fetching the paper…
Reading the bibliography…
We achieve very efficient deep learning model deployment that designs neural network architectures to fit different hardware constraints.
KENDALL, M.G.: A NEW MEASURE OF RANK CORRELATION. Biometrika 30
1938
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR09 (2009)
2009
Earlier work this paper cites.
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Proceedings of International Conference on Learning and Intelligent Optimization. pp. 507–523 (2011)
2011
Earlier work this paper cites.
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax (2016)
2016
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts (2016)
2016
Earlier work this paper cites.
Baker, B., Gupta, O., Naik, N., Raskar, R.: Designing neural network architectures using reinforcement learning. In: Proceedings of International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: Proceedings of International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 550–559. PMLR, Stockholmsmässan, Stockholm Sweden (10–15 Jul 2018)
2018
Earlier work this paper cites.
Dong, J.D., Cheng, A.C., Juan, D.C., Wei, W., Sun, M.: Dpp-net: Device-aware progressive search for pareto-optimal neural architectures. In: Proceedings of European Conference on Computer Vision (2018)
2018
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks (2018)
2018
Cited alongside, same era.
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive neural architecture search. In: Proceedings of European Conference on Computer Vision (2018)
2018
Cited alongside, same era.
Ma, N., Zhang, X., Zheng, H.T., Sun, J.: Shufflenet v2: Practical guidelines for efficient cnn architecture design. In: The European Conference on Computer Vision (ECCV) (September 2018)
2018
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q.V., Adam, H.: Searching for mobilenetv3. In: The IEEE International Conference on Computer Vision (ICCV) (October 2019)
2019
Later among the works it cites.
Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. In: Proceedings of International Conference on Learning Representation (2019)
2019
Later among the works it cites.
Stamoulis, D., Ding, R., Wang, D., Lymberopoulos, D., Priyantha, B., Liu, J., Marculescu, D.: Single-path nas: Designing hardware-efficient convnets in less than 4 hours (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Cai, H., Zhu, L., Han, S.: ProxylessNAS: Direct neural architecture search on target task and hardware. In: Proceedings of International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Chen, S., Chen, Y., Yan, S., Feng, J.: Efficient differentiable neural architecture search with meta kernels (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. Journal of Machine Learning Research 20
2019
Cited alongside, same era.
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., Le, Q.V.: Mnasnet: Platform-aware neural architecture search for mobile. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., Keutzer, K.: Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
Yan, S., Fang, B., Zhang, F., Zheng, Y., Zeng, X., Zhang, M., Xu, H.: Hm-nas: Efficient neural architecture search via hierarchical masking. In: The IEEE International Conference on Computer Vision (ICCV) Workshops (Oct 2019)
2019
Later among the works it cites.
Yu, K., Sciuto, C., Jaggi, M., Musat, C., Salzmann, M.: Evaluating the search phase of neural architecture search (2019)
2019
Later among the works it cites.
Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once for all: Train one network and specialize it for efficient deployment. In: Proceedings of International Conference on Learning Representations (2020)
2020
Closest in time.