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Size and Depth Separation in Approximating Benign Functions with Neural
  Networks

Size and Depth Separation in Approximating Benign Functions with Neural Networks

30 January 2021
Gal Vardi
Daniel Reichman
T. Pitassi
Ohad Shamir
ArXivPDFHTML

Papers citing "Size and Depth Separation in Approximating Benign Functions with Neural Networks"

4 / 4 papers shown
Title
Improved Bounds on Neural Complexity for Representing Piecewise Linear
  Functions
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
Kuan-Lin Chen
H. Garudadri
Bhaskar D. Rao
11
18
0
13 Oct 2022
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks
Sitan Chen
Aravind Gollakota
Adam R. Klivans
Raghu Meka
24
30
0
10 Feb 2022
The Connection Between Approximation, Depth Separation and Learnability
  in Neural Networks
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks
Eran Malach
Gilad Yehudai
Shai Shalev-Shwartz
Ohad Shamir
21
20
0
31 Jan 2021
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
148
602
0
14 Feb 2016
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