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On the Power of Over-parametrization in Neural Networks with Quadratic
  Activation

On the Power of Over-parametrization in Neural Networks with Quadratic Activation

3 March 2018
S. Du
J. Lee
ArXivPDFHTML

Papers citing "On the Power of Over-parametrization in Neural Networks with Quadratic Activation"

17 / 67 papers shown
Title
Width Provably Matters in Optimization for Deep Linear Neural Networks
Width Provably Matters in Optimization for Deep Linear Neural Networks
S. Du
Wei Hu
18
93
0
24 Jan 2019
On the Global Convergence of Imitation Learning: A Case for Linear
  Quadratic Regulator
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator
Qi Cai
Mingyi Hong
Yongxin Chen
Zhaoran Wang
19
34
0
11 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
  Networks
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou
Yuan Cao
Dongruo Zhou
Quanquan Gu
ODL
27
446
0
21 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient Descent Finds Global Minima of Deep Neural Networks
S. Du
J. Lee
Haochuan Li
Liwei Wang
Masayoshi Tomizuka
ODL
41
1,122
0
09 Nov 2018
Benefits of over-parameterization with EM
Benefits of over-parameterization with EM
Ji Xu
Daniel J. Hsu
A. Maleki
38
29
0
26 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei
J. Lee
Qiang Liu
Tengyu Ma
23
243
0
12 Oct 2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural
  Networks
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora
Nadav Cohen
Noah Golowich
Wei Hu
21
281
0
04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
S. Du
Xiyu Zhai
Barnabás Póczós
Aarti Singh
MLT
ODL
38
1,250
0
04 Oct 2018
Universal Approximation with Quadratic Deep Networks
Universal Approximation with Quadratic Deep Networks
Fenglei Fan
Jinjun Xiong
Ge Wang
PINN
20
78
0
31 Jul 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent
Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang
Yaodong Yu
Lingxiao Wang
Quanquan Gu
MLT
28
134
0
20 Jun 2018
Adding One Neuron Can Eliminate All Bad Local Minima
Adding One Neuron Can Eliminate All Bad Local Minima
Shiyu Liang
Ruoyu Sun
J. Lee
R. Srikant
31
89
0
22 May 2018
How Many Samples are Needed to Estimate a Convolutional or Recurrent
  Neural Network?
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
S. Du
Yining Wang
Xiyu Zhai
Sivaraman Balakrishnan
Ruslan Salakhutdinov
Aarti Singh
SSL
18
57
0
21 May 2018
Improved Learning of One-hidden-layer Convolutional Neural Networks with
  Overlaps
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
S. Du
Surbhi Goel
MLT
25
17
0
20 May 2018
Smoothed analysis for low-rank solutions to semidefinite programs in
  quadratic penalty form
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Srinadh Bhojanapalli
Nicolas Boumal
Prateek Jain
Praneeth Netrapalli
23
42
0
01 Mar 2018
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
148
602
0
14 Feb 2016
Norm-Based Capacity Control in Neural Networks
Norm-Based Capacity Control in Neural Networks
Behnam Neyshabur
Ryota Tomioka
Nathan Srebro
125
577
0
27 Feb 2015
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
183
1,185
0
30 Nov 2014
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