A Convergence Theory for Deep Learning via Over-Parameterization

9 November 2018

Papers citing "A Convergence Theory for Deep Learning via Over-Parameterization"

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Title
Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak Lechao Xiao Jiri Hron Jaehoon Lee Alexander A. Alemi Jascha Narain Sohl-Dickstein S. Schoenholz 38 225 0 05 Dec 2019
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
Adaptive dynamic programming for nonaffine nonlinear optimal control problem with state constraints Jingliang Duan Zhengyu Liu Shengbo Eben Li Qi Sun Zhenzhong Jia B. Cheng 15 64 0 26 Nov 2019
Neural Contextual Bandits with UCB-based Exploration Dongruo Zhou Lihong Li Quanquan Gu 36 15 0 11 Nov 2019
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li Ruosong Wang Dingli Yu S. Du Wei Hu Ruslan Salakhutdinov Sanjeev Arora 21 131 0 03 Nov 2019
Global Convergence of Gradient Descent for Deep Linear Residual Networks Lei Wu Qingcan Wang Chao Ma ODL AI4CE 28 22 0 02 Nov 2019
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval Yan Shuo Tan Roman Vershynin 22 35 0 28 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 40 44 0 15 Oct 2019
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks Spencer Frei Yuan Cao Quanquan Gu ODL 9 31 0 07 Oct 2019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks Sanjeev Arora S. Du Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang Dingli Yu AAML 19 161 0 03 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 24 116 0 03 Oct 2019
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 29 113 0 25 Sep 2019
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction Pan Xu F. Gao Quanquan Gu 31 83 0 18 Sep 2019
Stochastic AUC Maximization with Deep Neural Networks Mingrui Liu Zhuoning Yuan Yiming Ying Tianbao Yang 17 103 0 28 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 60 626 0 14 Aug 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 52 324 0 13 Jun 2019
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan J. Lee Daniel Soudry Nathan Srebro 30 353 0 13 Jun 2019
Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation Yu-Wei Kao Hung-Hsuan Chen BDL 20 5 0 13 Jun 2019
Parameterized Structured Pruning for Deep Neural Networks Günther Schindler Wolfgang Roth Franz Pernkopf Holger Froening 24 6 0 12 Jun 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 19 383 0 30 May 2019
Generalization bounds for deep convolutional neural networks Philip M. Long Hanie Sedghi MLT 42 89 0 29 May 2019
Enhancing Adversarial Defense by k-Winners-Take-All Chang Xiao Peilin Zhong Changxi Zheng AAML 24 97 0 25 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 24 183 0 24 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems Atsushi Nitanda Geoffrey Chinot Taiji Suzuki MLT 16 33 0 23 May 2019
A type of generalization error induced by initialization in deep neural networks Yaoyu Zhang Zhi-Qin John Xu Tao Luo Zheng Ma 9 49 0 19 May 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei Tengyu Ma 25 109 0 09 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 18 241 0 27 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 44 905 0 26 Apr 2019
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction Yeqi Liu Chuanyang Gong Ling Yang Yingyi Chen AI4TS 19 305 0 16 Apr 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 38 136 0 10 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 37 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 24 18 0 07 Apr 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 19 101 0 02 Apr 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 26 181 0 01 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset R. Tibshirani 31 728 0 19 Mar 2019
A Priori Estimates of the Population Risk for Residual Networks E. Weinan Chao Ma Qingcan Wang UQCV 37 61 0 06 Mar 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 34 1,077 0 18 Feb 2019
Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes Ali Hirsa T. Karatas Amir Oskoui 16 26 0 15 Feb 2019
Understanding over-parameterized deep networks by geometrization Xiao Dong Ling Zhou GNN AI4CE 21 7 0 11 Feb 2019
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs D. Gilboa B. Chang Minmin Chen Greg Yang S. Schoenholz Ed H. Chi Jeffrey Pennington 34 40 0 25 Jan 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 55 961 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 21 94 0 24 Jan 2019
Scaling description of generalization with number of parameters in deep learning Mario Geiger Arthur Jacot S. Spigler Franck Gabriel Levent Sagun Stéphane dÁscoli Giulio Biroli Clément Hongler M. Wyart 52 195 0 06 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 33 446 0 21 Nov 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 32 765 0 12 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 44 1,125 0 09 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 23 191 0 29 Oct 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 26 117 0 17 Oct 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 36 57 0 16 Oct 2018