暂无分享,去创建一个
[1] Quoc V. Le,et al. Understanding and Simplifying One-Shot Architecture Search , 2018, ICML.
[2] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[3] Ping Luo,et al. Differentiable Learning-to-Normalize via Switchable Normalization , 2018, ICLR.
[4] Shankar Krishnan,et al. Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] John Langford,et al. Efficient Forward Architecture Search , 2019, NeurIPS.
[6] Quoc V. Le,et al. Randaugment: Practical automated data augmentation with a reduced search space , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[7] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[8] Risto Miikkulainen,et al. Efficient Reinforcement Learning Through Evolving Neural Network Topologies , 2002, GECCO.
[9] Qi Tian,et al. Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[10] Frank Hutter,et al. Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..
[11] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Kaiming He,et al. Exploring Randomly Wired Neural Networks for Image Recognition , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[13] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[14] Ramesh Raskar,et al. Designing Neural Network Architectures using Reinforcement Learning , 2016, ICLR.
[15] Frank Hutter,et al. Multi-objective Architecture Search for CNNs , 2018, ArXiv.
[16] Sepp Hochreiter,et al. Self-Normalizing Neural Networks , 2017, NIPS.
[17] Sanjeev Arora,et al. An Exponential Learning Rate Schedule for Deep Learning , 2020, ICLR.
[18] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[19] Quoc V. Le,et al. Searching for Activation Functions , 2018, arXiv.
[20] Quoc V. Le,et al. AutoML-Zero: Evolving Machine Learning Algorithms From Scratch , 2020, ICML.
[21] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[23] Chuang Gan,et al. Once for All: Train One Network and Specialize it for Efficient Deployment , 2019, ICLR.
[24] Frank Hutter,et al. Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution , 2018, ICLR.
[25] Ameet Talwalkar,et al. Random Search and Reproducibility for Neural Architecture Search , 2019, UAI.
[26] Kalyanmoy Deb,et al. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.
[27] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[28] Julian Togelius,et al. Evolving Memory Cell Structures for Sequence Learning , 2009, ICANN.
[29] Quoc V. Le,et al. Searching for MobileNetV3 , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[30] Samuel L. Smith,et al. Batch Normalization Biases Deep Residual Networks Towards Shallow Paths , 2020, ArXiv.
[31] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[32] Quoc V. Le,et al. RandAugment: Practical data augmentation with no separate search , 2019, ArXiv.
[33] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[34] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[35] Kaiming He,et al. Group Normalization , 2018, ECCV.
[36] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[39] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[40] Tie-Yan Liu,et al. Neural Architecture Optimization , 2018, NeurIPS.
[41] Quoc V. Le,et al. SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Garrison W. Cottrell,et al. ReZero is All You Need: Fast Convergence at Large Depth , 2020, UAI.
[43] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[44] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[45] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[46] Kevin Gimpel,et al. Gaussian Error Linear Units (GELUs) , 2016 .
[47] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[48] Jie Liu,et al. Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours , 2019, ECML/PKDD.
[49] Kaiming He,et al. Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[50] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[51] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[52] Theodore Lim,et al. SMASH: One-Shot Model Architecture Search through HyperNetworks , 2017, ICLR.
[53] Xiangyu Zhang,et al. Single Path One-Shot Neural Architecture Search with Uniform Sampling , 2019, ECCV.
[54] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[55] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[56] Kaiming He,et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.
[57] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[58] Elad Hoffer,et al. Norm matters: efficient and accurate normalization schemes in deep networks , 2018, NeurIPS.
[59] Tengyu Ma,et al. Fixup Initialization: Residual Learning Without Normalization , 2019, ICLR.
[60] Liang Lin,et al. SNAS: Stochastic Neural Architecture Search , 2018, ICLR.
[61] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[62] Oriol Vinyals,et al. Hierarchical Representations for Efficient Architecture Search , 2017, ICLR.
[63] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[64] Samuel L. Smith,et al. Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks , 2020, NeurIPS.
[65] Yuandong Tian,et al. FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[66] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[67] Kenji Doya,et al. Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , 2017, Neural Networks.