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1901.08572
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Width Provably Matters in Optimization for Deep Linear Neural Networks
24 January 2019
S. Du
Wei Hu
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Papers citing
"Width Provably Matters in Optimization for Deep Linear Neural Networks"
20 / 70 papers shown
Which Minimizer Does My Neural Network Converge To?
Manuel Nonnenmacher
David Reeb
Ingo Steinwart
ODL
193
5
0
04 Nov 2020
A Unifying View on Implicit Bias in Training Linear Neural Networks
International Conference on Learning Representations (ICLR), 2020
Chulhee Yun
Shankar Krishnan
H. Mobahi
MLT
461
90
0
06 Oct 2020
A Modular Analysis of Provable Acceleration via Polyak's Momentum: Training a Wide ReLU Network and a Deep Linear Network
International Conference on Machine Learning (ICML), 2020
Jun-Kun Wang
Chi-Heng Lin
Jacob D. Abernethy
600
24
0
04 Oct 2020
Deep matrix factorizations
Computer Science Review (CSR), 2020
Pierre De Handschutter
Nicolas Gillis
Xavier Siebert
BDL
421
56
0
01 Oct 2020
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Neural Information Processing Systems (NeurIPS), 2020
Chandrashekar Lakshminarayanan
Amit Singh
AI4CE
177
11
0
11 Jun 2020
Analysis of Knowledge Transfer in Kernel Regime
International Conference on Information and Knowledge Management (CIKM), 2020
Arman Rahbar
Ashkan Panahi
Chiranjib Bhattacharyya
Devdatt Dubhashi
M. Chehreghani
167
4
0
30 Mar 2020
On the Global Convergence of Training Deep Linear ResNets
International Conference on Learning Representations (ICLR), 2020
Difan Zou
Philip M. Long
Quanquan Gu
186
41
0
02 Mar 2020
Revealing the Structure of Deep Neural Networks via Convex Duality
International Conference on Machine Learning (ICML), 2020
Tolga Ergen
Mert Pilanci
MLT
418
74
0
22 Feb 2020
Deep Gated Networks: A framework to understand training and generalisation in deep learning
Chandrashekar Lakshminarayanan
Amit Singh
AI4CE
98
2
0
10 Feb 2020
Distribution Approximation and Statistical Estimation Guarantees of Generative Adversarial Networks
Minshuo Chen
Wenjing Liao
H. Zha
Tuo Zhao
265
20
0
10 Feb 2020
Quasi-Equivalence of Width and Depth of Neural Networks
Fenglei Fan
Rongjie Lai
Ge Wang
569
12
0
06 Feb 2020
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
International Conference on Learning Representations (ICLR), 2020
Wei Hu
Lechao Xiao
Jeffrey Pennington
198
128
0
16 Jan 2020
Global Convergence of Gradient Descent for Deep Linear Residual Networks
Neural Information Processing Systems (NeurIPS), 2019
Lei Wu
Qingcan Wang
Chao Ma
ODL
AI4CE
235
24
0
02 Nov 2019
Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks
Analysis and Applications (Anal. Appl.), 2019
Yeonjong Shin
284
13
0
14 Oct 2019
Quadratic Suffices for Over-parametrization via Matrix Chernoff Bound
Zhao Song
Xin Yang
150
96
0
09 Jun 2019
Implicit Regularization in Deep Matrix Factorization
Neural Information Processing Systems (NeurIPS), 2019
Sanjeev Arora
Nadav Cohen
Wei Hu
Yuping Luo
AI4CE
392
561
0
31 May 2019
On Exact Computation with an Infinitely Wide Neural Net
Sanjeev Arora
S. Du
Wei Hu
Zhiyuan Li
Ruslan Salakhutdinov
Ruosong Wang
641
991
0
26 Apr 2019
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections
E. Weinan
Chao Ma
Qingcan Wang
Lei Wu
MLT
267
22
0
10 Apr 2019
Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning
Kenji Kawaguchi
Jiaoyang Huang
L. Kaelbling
AAML
210
19
0
07 Apr 2019
Elimination of All Bad Local Minima in Deep Learning
Kenji Kawaguchi
L. Kaelbling
308
48
0
02 Jan 2019
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