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Neural Tangent Kernel: Convergence and Generalization in Neural Networks

20 June 2018

Papers citing "Neural Tangent Kernel: Convergence and Generalization in Neural Networks"

50 / 1,163 papers shown

Title
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels S. Du Kangcheng Hou Barnabás Póczós Ruslan Salakhutdinov Ruosong Wang Keyulu Xu 142 276 0 30 May 2019
Norm-based generalisation bounds for multi-class convolutional neural networks Antoine Ledent Waleed Mustafa Yunwen Lei Marius Kloft 66 5 0 29 May 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 110 260 0 29 May 2019
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems Tianle Cai Ruiqi Gao Jikai Hou Siyu Chen Dong Wang Di He Zhihua Zhang Liwei Wang ODL 67 57 0 28 May 2019
Infinitely deep neural networks as diffusion processes Stefano Peluchetti Stefano Favaro ODL 105 32 0 27 May 2019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks Guodong Zhang James Martens Roger C. Grosse ODL 109 126 0 27 May 2019
Temporal-difference learning with nonlinear function approximation: lazy training and mean field regimes Andrea Agazzi Jianfeng Lu 60 8 0 27 May 2019
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm S. Spigler Mario Geiger Matthieu Wyart 110 38 0 26 May 2019
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective Lili Su Pengkun Yang MLT 73 54 0 26 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 416 183 0 24 May 2019
Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks Yaoyu Zhang Zhi-Qin John Xu Yaoyu Zhang Zheng Ma MLT AI4CE 130 38 0 24 May 2019
Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima Qi Cai Zhuoran Yang Jason D. Lee Zhaoran Wang 66 30 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 93 34 0 23 May 2019
A type of generalization error induced by initialization in deep neural networks Yaoyu Zhang Zhi-Qin John Xu Yaoyu Zhang Zheng Ma 128 51 0 19 May 2019
An Information Theoretic Interpretation to Deep Neural Networks Shao-Lun Huang Xiangxiang Xu Lizhong Zheng G. Wornell FAtt 87 44 0 16 May 2019
Do Kernel and Neural Embeddings Help in Training and Generalization? Arman Rahbar Emilio Jorge Devdatt Dubhashi Morteza Haghir Chehreghani MLT 97 0 0 13 May 2019
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study Daniel S. Park Jascha Narain Sohl-Dickstein Quoc V. Le Samuel L. Smith 96 57 0 09 May 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei Tengyu Ma 85 110 0 09 May 2019
Similarity of Neural Network Representations Revisited Simon Kornblith Mohammad Norouzi Honglak Lee Geoffrey E. Hinton 161 1,438 0 01 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 93 243 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 253 928 0 26 Apr 2019
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process Guy Blanc Neha Gupta Gregory Valiant Paul Valiant 163 147 0 19 Apr 2019
The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent Karthik A. Sankararaman Soham De Zheng Xu Wenjie Huang Tom Goldstein ODL 106 105 0 15 Apr 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 198 135 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 103 22 0 10 Apr 2019
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics E. Weinan Chao Ma Lei Wu MLT 74 123 0 08 Apr 2019
Convergence rates for the stochastic gradient descent method for non-convex objective functions Benjamin J. Fehrman Benjamin Gess Arnulf Jentzen 96 101 0 02 Apr 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 123 182 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 125 354 0 27 Mar 2019
Towards Characterizing Divergence in Deep Q-Learning Joshua Achiam Ethan Knight Pieter Abbeel 55 98 0 21 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 260 747 0 19 Mar 2019
Stabilize Deep ResNet with A Sharp Scaling Factor $τ$ Huishuai Zhang Da Yu Mingyang Yi Wei Chen Tie-Yan Liu 57 9 0 17 Mar 2019
Mean Field Analysis of Deep Neural Networks Justin A. Sirignano K. Spiliopoulos 93 82 0 11 Mar 2019
Function Space Particle Optimization for Bayesian Neural Networks Ziyu Wang Zhaolin Ren Jun Zhu Bo Zhang BDL 63 65 0 26 Feb 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 218 1,111 0 18 Feb 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 90 279 0 16 Feb 2019
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang 182 289 0 13 Feb 2019
Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan J. Zico Kolter MoMe AI4CE 92 317 0 13 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks Phan-Minh Nguyen AI4CE 76 72 0 07 Feb 2019
Are All Layers Created Equal? Chiyuan Zhang Samy Bengio Y. Singer 109 140 0 06 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 119 158 0 04 Feb 2019
Stiffness: A New Perspective on Generalization in Neural Networks Stanislav Fort Pawel Krzysztof Nowak Stanislaw Jastrzebski S. Narayanan 131 94 0 28 Jan 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 86 42 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 232 974 0 24 Jan 2019
A Theoretical Analysis of Deep Q-Learning Jianqing Fan Zhuoran Yang Yuchen Xie Zhaoran Wang 190 610 0 01 Jan 2019
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins Dawei Li Tian Ding Ruoyu Sun 114 38 0 28 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 840 0 19 Dec 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 219 775 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 301 1,470 0 09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 263 1,136 0 09 Nov 2018