Efficient Neural Architecture Search via Proximal Iterations
暂无分享,去创建一个
[1] Richard Socher,et al. Regularizing and Optimizing LSTM Language Models , 2017, ICLR.
[2] H. Sebastian Seung,et al. Learning the parts of objects by non-negative matrix factorization , 1999, Nature.
[3] Chris H. Q. Ding,et al. Binary Matrix Factorization with Applications , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[4] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Jürgen Schmidhuber,et al. Recurrent Highway Networks , 2016, ICML.
[6] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Lihi Zelnik-Manor,et al. ASAP: Architecture Search, Anneal and Prune , 2019, AISTATS.
[8] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Lin Xiao,et al. Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..
[10] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[11] Ya Le,et al. Tiny ImageNet Visual Recognition Challenge , 2015 .
[12] Xiangyu Zhang,et al. Single Path One-Shot Neural Architecture Search with Uniform Sampling , 2019, ECCV.
[13] Nicolas Usunier,et al. Improving Neural Language Models with a Continuous Cache , 2016, ICLR.
[14] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[15] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[17] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[18] Chris Dyer,et al. On the State of the Art of Evaluation in Neural Language Models , 2017, ICLR.
[19] Alan L. Yuille,et al. Genetic CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[21] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[22] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[23] Tie-Yan Liu,et al. Neural Architecture Optimization , 2018, NeurIPS.
[24] Dan Alistarh,et al. QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks , 2016, 1610.02132.
[25] Ruslan Salakhutdinov,et al. Breaking the Softmax Bottleneck: A High-Rank RNN Language Model , 2017, ICLR.
[26] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[27] Yi Yang,et al. Searching for a Robust Neural Architecture in Four GPU Hours , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.
[29] Hakan Inan,et al. Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling , 2016, ICLR.
[30] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[31] James T. Kwok,et al. Loss-aware Binarization of Deep Networks , 2016, ICLR.
[32] Saman Ghili,et al. Tiny ImageNet Visual Recognition Challenge , 2014 .
[33] Quoc V. Le,et al. Understanding and Simplifying One-Shot Architecture Search , 2018, ICML.
[34] Wei Wu,et al. Practical Block-Wise Neural Network Architecture Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[35] Isabelle Guyon,et al. Taking Human out of Learning Applications: A Survey on Automated Machine Learning , 2018, 1810.13306.
[36] Mark J. F. Gales,et al. Predictive Uncertainty Estimation via Prior Networks , 2018, NeurIPS.
[37] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..
[38] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Tie-Yan Liu,et al. Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems , 2016, IJCAI.
[40] Yu Bai,et al. ProxQuant: Quantized Neural Networks via Proximal Operators , 2018, ICLR.
[41] Oriol Vinyals,et al. Hierarchical Representations for Efficient Architecture Search , 2017, ICLR.
[42] Liang Lin,et al. SNAS: Stochastic Neural Architecture Search , 2018, ICLR.
[43] Yoram Singer,et al. Efficient projections onto the l1-ball for learning in high dimensions , 2008, ICML '08.
[44] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[45] Ben Poole,et al. Categorical Reparameterization with Gumbel-Softmax , 2016, ICLR.
[46] Quoc V. Le,et al. AutoAugment: Learning Augmentation Policies from Data , 2018, ArXiv.
[47] Dimitri P. Bertsekas,et al. Nonlinear Programming , 1997 .
[48] Graham W. Taylor,et al. Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.
[49] Wei Pan,et al. BayesNAS: A Bayesian Approach for Neural Architecture Search , 2019, ICML.
[50] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[51] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[52] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[53] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[54] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[55] Youhei Akimoto,et al. Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search , 2019, ICML.
[56] Dawn Xiaodong Song,et al. Differentiable Neural Network Architecture Search , 2018, ICLR.