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Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial
  Convergence and SQ Lower Bounds

Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds

7 May 2018
Santosh Vempala
John Wilmes
    MLT
ArXivPDFHTML

Papers citing "Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds"

15 / 15 papers shown
Title
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur
  Polynomials
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials
Ilias Diakonikolas
D. Kane
32
4
0
24 Jul 2023
A faster and simpler algorithm for learning shallow networks
A faster and simpler algorithm for learning shallow networks
Sitan Chen
Shyam Narayanan
41
7
0
24 Jul 2023
Computational Complexity of Learning Neural Networks: Smoothness and
  Degeneracy
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
Amit Daniely
Nathan Srebro
Gal Vardi
33
4
0
15 Feb 2023
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
27
30
0
10 Feb 2022
From Local Pseudorandom Generators to Hardness of Learning
From Local Pseudorandom Generators to Hardness of Learning
Amit Daniely
Gal Vardi
109
30
0
20 Jan 2021
Towards Understanding Ensemble, Knowledge Distillation and
  Self-Distillation in Deep Learning
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
FedML
60
356
0
17 Dec 2020
Small Covers for Near-Zero Sets of Polynomials and Learning Latent
  Variable Models
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models
Ilias Diakonikolas
D. Kane
21
32
0
14 Dec 2020
Gradient Starvation: A Learning Proclivity in Neural Networks
Gradient Starvation: A Learning Proclivity in Neural Networks
Mohammad Pezeshki
Sekouba Kaba
Yoshua Bengio
Aaron Courville
Doina Precup
Guillaume Lajoie
MLT
50
258
0
18 Nov 2020
Learning Deep ReLU Networks Is Fixed-Parameter Tractable
Learning Deep ReLU Networks Is Fixed-Parameter Tractable
Sitan Chen
Adam R. Klivans
Raghu Meka
22
36
0
28 Sep 2020
Feature Purification: How Adversarial Training Performs Robust Deep
  Learning
Feature Purification: How Adversarial Training Performs Robust Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
MLT
AAML
37
147
0
20 May 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri
Meirav Galun
Amnon Geifman
David Jacobs
Yoni Kasten
S. Kritchman
39
183
0
10 Mar 2020
Towards Understanding the Spectral Bias of Deep Learning
Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao
Zhiying Fang
Yue Wu
Ding-Xuan Zhou
Quanquan Gu
41
215
0
03 Dec 2019
The generalization error of random features regression: Precise
  asymptotics and double descent curve
The generalization error of random features regression: Precise asymptotics and double descent curve
Song Mei
Andrea Montanari
60
626
0
14 Aug 2019
Linearized two-layers neural networks in high dimension
Linearized two-layers neural networks in high dimension
Behrooz Ghorbani
Song Mei
Theodor Misiakiewicz
Andrea Montanari
MLT
18
241
0
27 Apr 2019
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
153
603
0
14 Feb 2016
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