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Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity

Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity

Pritish Kamath
Omar Montasser
Nathan Srebro
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Papers citing "Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity"

5 / 5 papers shown
Title
What Can ResNet Learn Efficiently, Going Beyond Kernels?
What Can ResNet Learn Efficiently, Going Beyond Kernels?
Zeyuan Allen-Zhu
Yuanzhi Li
390
183
0
24 May 2019
On the Power and Limitations of Random Features for Understanding Neural Networks
On the Power and Limitations of Random Features for Understanding Neural Networks
Gilad Yehudai
Ohad Shamir
MLT
72
182
0
01 Apr 2019
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot
Franck Gabriel
Clément Hongler
267
3,195
0
20 Jun 2018
SGD Learns the Conjugate Kernel Class of the Network
SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely
184
181
0
27 Feb 2017
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits
D. Kane
Ryan Williams
34
61
0
24 Nov 2015