ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1510.03528
  4. Cited By
$\ell_1$-regularized Neural Networks are Improperly Learnable in
  Polynomial Time

ℓ1\ell_1ℓ1​-regularized Neural Networks are Improperly Learnable in Polynomial Time

13 October 2015
Yuchen Zhang
J. Lee
Michael I. Jordan
ArXivPDFHTML

Papers citing "$\ell_1$-regularized Neural Networks are Improperly Learnable in Polynomial Time"

17 / 67 papers shown
Title
Simple Recurrent Units for Highly Parallelizable Recurrence
Simple Recurrent Units for Highly Parallelizable Recurrence
Tao Lei
Yu Zhang
Sida I. Wang
Huijing Dai
Yoav Artzi
LRM
44
271
0
08 Sep 2017
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural
  Networks
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
Surbhi Goel
Adam R. Klivans
22
27
0
11 Aug 2017
Theoretical insights into the optimization landscape of
  over-parameterized shallow neural networks
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi
Adel Javanmard
J. Lee
36
415
0
16 Jul 2017
Recovery Guarantees for One-hidden-layer Neural Networks
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong
Zhao Song
Prateek Jain
Peter L. Bartlett
Inderjit S. Dhillon
MLT
34
336
0
10 Jun 2017
Group Invariance, Stability to Deformations, and Complexity of Deep
  Convolutional Representations
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations
A. Bietti
Julien Mairal
26
8
0
09 Jun 2017
Weight Sharing is Crucial to Succesful Optimization
Weight Sharing is Crucial to Succesful Optimization
Shai Shalev-Shwartz
Ohad Shamir
Shaked Shammah
36
12
0
02 Jun 2017
Deriving Neural Architectures from Sequence and Graph Kernels
Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei
Wengong Jin
Regina Barzilay
Tommi Jaakkola
GNN
45
137
0
25 May 2017
The Landscape of Deep Learning Algorithms
The Landscape of Deep Learning Algorithms
Pan Zhou
Jiashi Feng
12
24
0
19 May 2017
SGD Learns the Conjugate Kernel Class of the Network
SGD Learns the Conjugate Kernel Class of the Network
Amit Daniely
6
181
0
27 Feb 2017
Convergence Results for Neural Networks via Electrodynamics
Convergence Results for Neural Networks via Electrodynamics
Rina Panigrahy
Sushant Sachdeva
Qiuyi Zhang
MLT
MDE
29
22
0
01 Feb 2017
Deep Function Machines: Generalized Neural Networks for Topological
  Layer Expression
Deep Function Machines: Generalized Neural Networks for Topological Layer Expression
William H. Guss
AI4CE
14
12
0
14 Dec 2016
Convexified Convolutional Neural Networks
Convexified Convolutional Neural Networks
Yuchen Zhang
Percy Liang
Martin J. Wainwright
26
64
0
04 Sep 2016
Risk Bounds for High-dimensional Ridge Function Combinations Including
  Neural Networks
Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks
Jason M. Klusowski
Andrew R. Barron
32
69
0
05 Jul 2016
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
AdaNet: Adaptive Structural Learning of Artificial Neural Networks
Corinna Cortes
X. Gonzalvo
Vitaly Kuznetsov
M. Mohri
Scott Yang
29
282
0
05 Jul 2016
Training Recurrent Neural Networks by Diffusion
Training Recurrent Neural Networks by Diffusion
H. Mobahi
ODL
14
46
0
16 Jan 2016
Learning Halfspaces and Neural Networks with Random Initialization
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang
J. Lee
Martin J. Wainwright
Michael I. Jordan
17
35
0
25 Nov 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
Previous
12