Neural Architecture Search with Reinforcement Learning.

Articles citing Neural Architecture Search with Reinforcement Learning.

Title / Author	Year
Squeeze-and-excitation networks J Hu, L Shen, G Sun Proceedings of the IEEE conference on computer vision and pattern …	2018
Mobilenetv2: Inverted residuals and linear bottlenecks M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Proceedings of the IEEE conference on computer vision and pattern …	2018
Learning transferable architectures for scalable image recognition B Zoph, V Vasudevan, J Shlens, QV Le Proceedings of the IEEE conference on computer vision and pattern …	2018
Efficient neural architecture search via parameter sharing H Pham, MY Guan, B Zoph, QV Le, J Dean arXiv preprint arXiv:1802.03268	2018
Darts: Differentiable architecture search H Liu, K Simonyan, Y Yang arXiv preprint arXiv:1806.09055	2018
Regularized evolution for image classifier architecture search E Real, A Aggarwal, Y Huang, QV Le Proceedings of the aaai conference on artificial intelligence 33, 4780-4789	2019
Progressive neural architecture search C Liu, B Zoph, M Neumann, J Shlens, W Hua, LJ Li, L Fei-Fei, A Yuille, ... Proceedings of the European Conference on Computer Vision (ECCV), 19-34	2018
Large-scale evolution of image classifiers E Real, S Moore, A Selle, S Saxena, YL Suematsu, J Tan, Q Le, A Kurakin arXiv preprint arXiv:1703.01041	2017
Shufflenet v2: Practical guidelines for efficient cnn architecture design N Ma, X Zhang, HT Zheng, J Sun Proceedings of the European conference on computer vision (ECCV), 116-131	2018
Deep learning with convolutional neural networks for EEG decoding and visualization RT Schirrmeister, JT Springenberg, LDJ Fiederer, M Glasstetter, ... Human brain mapping 38 (11), 5391-5420	2017
Efficientnet: Rethinking model scaling for convolutional neural networks M Tan, QV Le arXiv preprint arXiv:1905.11946	2019
Learning efficient convolutional networks through network slimming Z Liu, J Li, Z Shen, G Huang, S Yan, C Zhang Proceedings of the IEEE International Conference on Computer Vision, 2736-2744	2017
Searching for activation functions P Ramachandran, B Zoph, QV Le arXiv preprint arXiv:1710.05941	2017
Regularizing and optimizing LSTM language models S Merity, NS Keskar, R Socher arXiv preprint arXiv:1708.02182	2017
Mnasnet: Platform-aware neural architecture search for mobile M Tan, B Chen, R Pang, V Vasudevan, M Sandler, A Howard, QV Le Proceedings of the IEEE Conference on Computer Vision and Pattern …	2019
Transformer-xl: Attentive language models beyond a fixed-length context Z Dai, Z Yang, Y Yang, J Carbonell, QV Le, R Salakhutdinov arXiv preprint arXiv:1901.02860	2019
Deep reinforcement learning: An overview Y Li arXiv preprint arXiv:1701.07274	2017
Deep reinforcement learning: A brief survey K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath IEEE Signal Processing Magazine 34 (6), 26-38	2017
Using the output embedding to improve language models O Press, L Wolf arXiv preprint arXiv:1608.05859	2016
Autoaugment: Learning augmentation policies from data ED Cubuk, B Zoph, D Mane, V Vasudevan, QV Le arXiv preprint arXiv:1805.09501	2018

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