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[1] Geoffrey E. Hinton,et al. Lookahead Optimizer: k steps forward, 1 step back , 2019, NeurIPS.
[2] Matus Telgarsky,et al. Spectrally-normalized margin bounds for neural networks , 2017, NIPS.
[3] Liyuan Liu,et al. On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.
[4] Xiaohua Zhai,et al. A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark , 2019 .
[5] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[6] Lei Zhang,et al. Gradient Centralization: A New Optimization Technique for Deep Neural Networks , 2020, ECCV.
[7] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[9] David A. McAllester,et al. A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks , 2017, ICLR.
[10] Lawrence D. Jackel,et al. Learning Curves: Asymptotic Values and Rate of Convergence , 1993, NIPS.
[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Bolei Zhou,et al. Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[16] G. Gnecco,et al. Approximation Error Bounds via Rademacher's Complexity , 2008 .
[17] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[18] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[19] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] Yang Yang,et al. Deep Learning Scaling is Predictable, Empirically , 2017, ArXiv.
[21] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[22] Kaiming He,et al. Rethinking ImageNet Pre-Training , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[23] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[24] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[25] Jonathan S. Rosenfeld,et al. A Constructive Prediction of the Generalization Error Across Scales , 2020, ICLR.
[26] Yi Zhang,et al. Stronger generalization bounds for deep nets via a compression approach , 2018, ICML.
[27] Pedro M. Domingos. A Unified Bias-Variance Decomposition , 2022 .
[28] Alec Radford,et al. Scaling Laws for Neural Language Models , 2020, ArXiv.