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We study the relationship between the frequency of a function and the speed at which a neural network learns it.
Notes on spherical harmonics and linear representations of lie groups
Jean Gallier · 2009
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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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The power of depth for feedforward neural networks
Ronen Eldan and Ohad Shamir · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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benefits of depth in neural networks
Matus Telgarsky · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Weight sharing is crucial to succesful optimization
Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah · 2017
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Diverse neural network learns true target functions
Bo Xie, Yingyu Liang, and Le Song · 2017
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On the convergence rate of training recurrent neural networks
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2018
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Sgd learns over-parameterized networks that provably generalize on linearly separable data
Alon Brutzkus, Amir Globerson, Eran Malach, and Shai Shalev-Shwartz · 2018
Cited alongside, same era.
A spectral approach to generalization and optimization in neural networks
Farzan Farnia, Jesse Zhang, and David Tse · 2018
Cited alongside, same era.
Implicit bias of gradient descent on linear convolutional networks
Suriya Gunasekar, Jason D Lee, Daniel Soudry, and Nati Srebro · 2018
Cited alongside, same era.
Gradient descent aligns the layers of deep linear networks
Ziwei Ji and Matus Telgarsky · 2018
Cited alongside, same era.
Understanding training and generalization in deep learning by fourier analysis
Zhiqin John Xu · 2018
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Stochastic gradient descent optimizes over-parameterized deep relu networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
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Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
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On lazy training in differentiable programming
Lenaic Chizat, Edouard Oyallon, and Francis Bach · 2019
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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Ziwei Ji and Matus Telgarsky · 2018
Cited alongside, same era.
Learning overparameterized neural networks via stochastic gradient descent on structured data
Yuanzhi Li and Yingyu Liang · 2018
Cited alongside, same era.
On the spectral bias of deep neural networks
Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A Hamprecht, Yoshua Bengio, and Aaron Courville · 2018
Cited alongside, same era.
The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
Cited alongside, same era.
Linearized two-layers neural networks in high dimension
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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Mingchen Li, Mahdi Soltanolkotabi, and Samet Oymak · 2019
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Frequency principle: Fourier analysis sheds light on deep neural networks
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, and Zheng Ma · 2019
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