暂无分享,去创建一个
Joshua M. Susskind | Shuangfei Zhai | Etai Littwin | Ben Myara | Sima Sabah | Oren Golan | J. Susskind | Shuangfei Zhai | Etai Littwin | Ben Myara | Sima Sabah | Oren Golan
[1] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[2] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Shai Shalev-Shwartz,et al. SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data , 2017, ICLR.
[4] Boris Hanin,et al. Finite Depth and Width Corrections to the Neural Tangent Kernel , 2019, ICLR.
[5] Wayne Luk,et al. Efficient Structured Pruning and Architecture Searching for Group Convolution , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[6] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[7] Greg Yang,et al. Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes , 2019, NeurIPS.
[8] Ethan Dyer,et al. Asymptotics of Wide Networks from Feynman Diagrams , 2019, ICLR.
[9] David Rolnick,et al. How to Start Training: The Effect of Initialization and Architecture , 2018, NeurIPS.
[10] Abraham J. Wyner,et al. Modern Neural Networks Generalize on Small Data Sets , 2018, NeurIPS.
[11] Ruosong Wang,et al. Enhanced Convolutional Neural Tangent Kernels , 2019, ArXiv.
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] Arthur Jacot,et al. Neural tangent kernel: convergence and generalization in neural networks (invited paper) , 2018, NeurIPS.
[14] Shiguang Shan,et al. Fully Learnable Group Convolution for Acceleration of Deep Neural Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[16] Ruosong Wang,et al. On Exact Computation with an Infinitely Wide Neural Net , 2019, NeurIPS.
[17] Jaehoon Lee,et al. Wide neural networks of any depth evolve as linear models under gradient descent , 2019, NeurIPS.
[18] Nathan Srebro,et al. Exploring Generalization in Deep Learning , 2017, NIPS.
[19] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[20] Boris Hanin,et al. Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients? , 2018, NeurIPS.
[21] Lior Wolf,et al. On the Convex Behavior of Deep Neural Networks in Relation to the Layers' Width , 2019, ArXiv.
[22] Kilian Q. Weinberger,et al. CondenseNet: An Efficient DenseNet Using Learned Group Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Frank Hutter,et al. A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets , 2017, ArXiv.