When is a Convolutional Filter Easy To Learn?

18 September 2017

Papers citing "When is a Convolutional Filter Easy To Learn?"

27 / 27 papers shown

Title
$How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance$ How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance Hongkang Li Shuai Zhang Yihua Zhang Meng Wang Sijia Liu Pin-Yu Chen 41 4 0 12 Mar 2024
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu S. Du 31 16 0 20 Feb 2023
Magnitude and Angle Dynamics in Training Single ReLU Neurons Sangmin Lee Byeongsu Sim Jong Chul Ye MLT 96 6 0 27 Sep 2022
Parameter identifiability of a deep feedforward ReLU neural network Joachim Bona-Pellissier François Bachoc François Malgouyres 41 14 0 24 Dec 2021
ReLU Regression with Massart Noise Ilias Diakonikolas Jongho Park Christos Tzamos 50 11 0 10 Sep 2021
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021
Learning Graph Neural Networks with Approximate Gradient Descent Qunwei Li Shaofeng Zou Leon Wenliang Zhong GNN 32 1 0 07 Dec 2020
Learning Deep ReLU Networks Is Fixed-Parameter Tractable Sitan Chen Adam R. Klivans Raghu Meka 22 36 0 28 Sep 2020
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 13 33 0 16 Jun 2020
An Optimization and Generalization Analysis for Max-Pooling Networks Alon Brutzkus Amir Globerson MLT AI4CE 11 4 0 22 Feb 2020
Convergence of End-to-End Training in Deep Unsupervised Contrastive Learning Zixin Wen SSL 21 2 0 17 Feb 2020
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 35 962 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 16 93 0 24 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 22 446 0 21 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang M. Tomizuka ODL 21 1,122 0 09 Nov 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 28 57 0 16 Oct 2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks Sanjeev Arora Nadav Cohen Noah Golowich Wei Hu 16 280 0 04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 33 1,251 0 04 Oct 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 28 134 0 20 Jun 2018
Adding One Neuron Can Eliminate All Bad Local Minima Shiyu Liang Ruoyu Sun J. Lee R. Srikant 29 89 0 22 May 2018
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network? S. Du Yining Wang Xiyu Zhai Sivaraman Balakrishnan Ruslan Salakhutdinov Aarti Singh SSL 13 57 0 21 May 2018
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps S. Du Surbhi Goel MLT 20 17 0 20 May 2018
End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition Samet Oymak Mahdi Soltanolkotabi 19 12 0 16 May 2018
The Global Optimization Geometry of Shallow Linear Neural Networks Zhihui Zhu Daniel Soudry Yonina C. Eldar M. Wakin ODL 18 36 0 13 May 2018
Learning Compact Neural Networks with Regularization Samet Oymak MLT 35 39 0 05 Feb 2018
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data Alon Brutzkus Amir Globerson Eran Malach Shai Shalev-Shwartz MLT 37 276 0 27 Oct 2017
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 179 1,185 0 30 Nov 2014