Why does Deep Learning work? - A perspective from Group Theory

20 December 2014

Papers citing "Why does Deep Learning work? - A perspective from Group Theory"

11 / 11 papers shown

Title
Deep Autoencoders: From Understanding to Generalization Guarantees Romain Cosentino Randall Balestriero Richard Baraniuk B. Aazhang 11 5 0 20 Sep 2020
Short sighted deep learning R. Koch Anita de Mello Koch Nicholas Kastanos Ling Cheng 14 8 0 07 Feb 2020
Is Deep Learning a Renormalization Group Flow? E. Koch R. Koch Ling Cheng OOD AI4CE 11 6 0 12 Jun 2019
Deep Learning for Cognitive Neuroscience Katherine R. Storrs N. Kriegeskorte NAI AI4CE 28 46 0 04 Mar 2019
Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow S. Iso Shotaro Shiba Sumito Yokoo OOD AI4CE 30 70 0 22 Jan 2018
On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition Jie Chen Chao Yuan M. Deng Chao Tao Jian Peng Haifeng Li 27 1 0 04 Aug 2017
Towards Understanding the Invertibility of Convolutional Neural Networks A. Gilbert Yi Zhang Kibok Lee Y. Zhang Honglak Lee 11 64 0 24 May 2017
How ConvNets model Non-linear Transformations Dipan K. Pal Marios Savvides 11 0 0 24 Feb 2017
Understanding trained CNNs by indexing neuron selectivity Ivet Rafegas M. Vanrell Luís A. Alexandre Guillem Arias FAtt 15 40 0 01 Feb 2017
PCANet: An energy perspective Jiasong Wu Shijie Qiu Youyong Kong Longyu Jiang L. Senhadji H. Shu 4 19 0 03 Mar 2016
Convergent Learning: Do different neural networks learn the same representations? Yixuan Li J. Yosinski Jeff Clune Hod Lipson J. Hopcroft SSL 37 354 0 24 Nov 2015