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A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
Annual Conference Computational Learning Theory (COLT), 2021
4 February 2021
Mo Zhou
Rong Ge
Chi Jin
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Papers citing
"A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network"
38 / 38 papers shown
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Henry Lam
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Depth Separation with Multilayer Mean-Field Networks
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Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron
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Marco Mondelli
Michael Rauchensteiner
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Learning Single-Index Models with Shallow Neural Networks
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When Expressivity Meets Trainability: Fewer than
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Mingyi Hong
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Global Convergence of SGD On Two Layer Neural Nets
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Parameter Convex Neural Networks
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Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
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Etienne Boursier
Loucas Pillaud-Vivien
Nicolas Flammarion
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Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods
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On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
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Itay Safran
Gal Vardi
Jason D. Lee
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286
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On Feature Learning in Neural Networks with Global Convergence Guarantees
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Zhengdao Chen
Eric Vanden-Eijnden
Joan Bruna
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332
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Parameter identifiability of a deep feedforward ReLU neural network
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Chahine Ibrahim
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On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
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Taiji Suzuki
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339
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A Geometric Analysis of Neural Collapse with Unconstrained Features
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Zhihui Zhu
Tianyu Ding
Jinxin Zhou
Xiao Li
Chong You
Jeremias Sulam
Qing Qu
358
251
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06 May 2021
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
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M. Fornasier
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Michael Rauchensteiner
OOD
248
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18 Jan 2021
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