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Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

12 October 2018

Papers citing "Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel"

42 / 192 papers shown

Title
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss Lénaïc Chizat Francis R. Bach MLT 273 341 0 11 Feb 2020
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks Zixiang Chen Yuan Cao Quanquan Gu Tong Zhang MLT 84 10 0 10 Feb 2020
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width Yu Bai Ben Krause Huan Wang Caiming Xiong R. Socher 84 22 0 10 Feb 2020
A Deep Conditioning Treatment of Neural Networks Naman Agarwal Pranjal Awasthi Satyen Kale AI4CE 119 16 0 04 Feb 2020
A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks Phan-Minh Nguyen H. Pham AI4CE 111 83 0 30 Jan 2020
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity Shiyu Liang Ruoyu Sun R. Srikant 83 20 0 31 Dec 2019
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks Aleksandr Shevchenko Marco Mondelli 196 38 0 20 Dec 2019
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 137 169 0 19 Dec 2019
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen Yuan Cao Difan Zou Quanquan Gu 77 123 0 27 Nov 2019
Convex Formulation of Overparameterized Deep Neural Networks Cong Fang Yihong Gu Weizhong Zhang Tong Zhang 83 28 0 18 Nov 2019
Adversarial Margin Maximization Networks Ziang Yan Yiwen Guo Changshui Zhang AAML 40 12 0 14 Nov 2019
Confident Learning: Estimating Uncertainty in Dataset Labels Curtis G. Northcutt Lu Jiang Isaac L. Chuang NoLa 229 705 0 31 Oct 2019
Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations Cong Fang Hanze Dong Tong Zhang 58 18 0 25 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 149 46 0 15 Oct 2019
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin Colin Wei Tengyu Ma AAML OOD 105 85 0 09 Oct 2019
A Function Space View of Bounded Norm Infinite Width ReLU Nets: The Multivariate Case Greg Ongie Rebecca Willett Daniel Soudry Nathan Srebro 113 161 0 03 Oct 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai Jason D. Lee 71 116 0 03 Oct 2019
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks Ziwei Ji Matus Telgarsky 98 178 0 26 Sep 2019
Finite Depth and Width Corrections to the Neural Tangent Kernel Boris Hanin Mihai Nica MDE 96 152 0 13 Sep 2019
Sparse Optimization on Measures with Over-parameterized Gradient Descent Lénaïc Chizat 97 93 0 24 Jul 2019
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao Colin Wei Adrien Gaidon Nikos Arechiga Tengyu Ma 141 1,617 0 18 Jun 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 114 336 0 13 Jun 2019
Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu Dimitris Papailiopoulos D. Achlioptas 100 81 0 06 Jun 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 149 392 0 30 May 2019
Temporal-difference learning with nonlinear function approximation: lazy training and mean field regimes Andrea Agazzi Jianfeng Lu 98 8 0 27 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 418 183 0 24 May 2019
Computationally Efficient Feature Significance and Importance for Machine Learning Models Enguerrand Horel K. Giesecke FAtt 55 9 0 23 May 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models Mor Shpigel Nacson Suriya Gunasekar Jason D. Lee Nathan Srebro Daniel Soudry 98 94 0 17 May 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei Tengyu Ma 98 110 0 09 May 2019
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process Guy Blanc Neha Gupta Gregory Valiant Paul Valiant 177 147 0 19 Apr 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 92 280 0 16 Feb 2019
Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks Phan-Minh Nguyen AI4CE 82 72 0 07 Feb 2019
Global convergence of neuron birth-death dynamics Grant M. Rotskoff Samy Jelassi Joan Bruna Eric Vanden-Eijnden 73 46 0 05 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 158 159 0 04 Feb 2019
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning Jian Li Xuanyuan Luo Mingda Qiao 77 89 0 02 Feb 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 245 974 0 24 Jan 2019
Understanding Geometry of Encoder-Decoder CNNs J. C. Ye Woon Kyoung Sung 3DV AI4CE 95 74 0 22 Jan 2019
Analysis of a Two-Layer Neural Network via Displacement Convexity Adel Javanmard Marco Mondelli Andrea Montanari MLT 119 57 0 05 Jan 2019
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins Dawei Li Tian Ding Ruoyu Sun 150 39 0 28 Dec 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 361 1,137 0 09 Nov 2018
A Priori Estimates of the Population Risk for Two-layer Neural Networks Weinan E Chao Ma Lei Wu 101 132 0 15 Oct 2018
Diffusion Scattering Transforms on Graphs Fernando Gama Alejandro Ribeiro Joan Bruna GNN 99 103 0 22 Jun 2018