Characterizing Implicit Bias in Terms of Optimization Geometry

22 February 2018

Papers citing "Characterizing Implicit Bias in Terms of Optimization Geometry"

25 / 75 papers shown

Title
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 6 33 0 16 Jun 2020
Shape Matters: Understanding the Implicit Bias of the Noise Covariance Jeff Z. HaoChen Colin Wei J. Lee Tengyu Ma 18 93 0 15 Jun 2020
To Each Optimizer a Norm, To Each Norm its Generalization Sharan Vaswani Reza Babanezhad Jose Gallego Aaron Mishkin Simon Lacoste-Julien Nicolas Le Roux 13 8 0 11 Jun 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 30 148 0 16 May 2020
Learning the gravitational force law and other analytic functions Atish Agarwala Abhimanyu Das Rina Panigrahy Qiuyi Zhang MLT 6 0 0 15 May 2020
Implicit Regularization in Deep Learning May Not Be Explainable by Norms Noam Razin Nadav Cohen 11 155 0 13 May 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss Lénaïc Chizat Francis R. Bach MLT 16 327 0 11 Feb 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 25 68 0 05 Feb 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 16 214 0 03 Dec 2019
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 11 116 0 03 Oct 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 8 353 0 13 Jun 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels Sai Praneeth Karimireddy Martin Jaggi 17 316 0 31 May 2019
Implicit Regularization in Deep Matrix Factorization Sanjeev Arora Nadav Cohen Wei Hu Yuping Luo AI4CE 24 491 0 31 May 2019
Generalization bounds for deep convolutional neural networks Philip M. Long Hanie Sedghi MLT 29 89 0 29 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
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 25 135 0 10 Apr 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 15 22 0 21 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 20 961 0 24 Jan 2019
Deep Geometric Prior for Surface Reconstruction Francis Williams T. Schneider Claudio Silva Denis Zorin Joan Bruna Daniele Panozzo 3DPC 14 189 0 27 Nov 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei J. Lee Qiang Liu Tengyu Ma 16 243 0 12 Oct 2018
Diffusion Scattering Transforms on Graphs Fernando Gama Alejandro Ribeiro Joan Bruna GNN 26 100 0 22 Jun 2018
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models? Tengyu Xu Yi Zhou Kaiyi Ji Yingbin Liang 13 19 0 12 Jun 2018
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate Mor Shpigel Nacson Nathan Srebro Daniel Soudry FedML MLT 11 97 0 05 Jun 2018
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization Navid Azizan B. Hassibi 8 61 0 04 Jun 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,886 0 15 Sep 2016