Seeing Convolution Through the Eyes of Finite Transformation Semigroup Theory: An Abstract Algebraic Interpretation of Convolutional Neural Networks

May 26, 2019

Andrew Hryniowski, Alexander Wong

May 26, 2019

Andrew Hryniowski, Alexander Wong

* 9 pages

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ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks

Jul 23, 2015

Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio

Jul 23, 2015

Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio

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4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

May 15, 2019

Christopher Choy, JunYoung Gwak, Silvio Savarese

May 15, 2019

Christopher Choy, JunYoung Gwak, Silvio Savarese

* CVPR'19

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* 9 pages, 2 figures

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Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition

Jan 13, 2018

Che-Wei Huang, Shrikanth. S. Narayanan

Jan 13, 2018

Che-Wei Huang, Shrikanth. S. Narayanan

* Revised Submission to IEEE Transactions

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Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

Dec 27, 2016

Zewang Zhang, Zheng Sun, Jiaqi Liu, Jingwen Chen, Zhao Huo, Xiao Zhang

Dec 27, 2016

Zewang Zhang, Zheng Sun, Jiaqi Liu, Jingwen Chen, Zhao Huo, Xiao Zhang

* 11 pages, 13 figures

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A Non-Technical Survey on Deep Convolutional Neural Network Architectures

Mar 06, 2018

Felix Altenberger, Claus Lenz

Mar 06, 2018

Felix Altenberger, Claus Lenz

* 17 pages (incl. references), 23 Postscript figures, uses IEEEtran

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Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition

May 31, 2018

Xikun Zhang, Chang Xu, Xinmei Tian, Dacheng Tao

May 31, 2018

Xikun Zhang, Chang Xu, Xinmei Tian, Dacheng Tao

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Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image

Jan 16, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

Jan 16, 2018

Lijun Zhao, Huihui Bai, Anhong Wang, Yao Zhao

* 11 pages, 7 figures

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Recent Advances in Convolutional Neural Networks

Oct 19, 2017

Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

Oct 19, 2017

Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Li Wang, Gang Wang, Jianfei Cai, Tsuhan Chen

* Pattern Recognition, Elsevier

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Convexified Convolutional Neural Networks

Sep 04, 2016

Yuchen Zhang, Percy Liang, Martin J. Wainwright

Sep 04, 2016

Yuchen Zhang, Percy Liang, Martin J. Wainwright

* 29 pages

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Combining Recurrent and Convolutional Neural Networks for Relation Classification

May 24, 2016

Ngoc Thang Vu, Heike Adel, Pankaj Gupta, Hinrich Schütze

May 24, 2016

Ngoc Thang Vu, Heike Adel, Pankaj Gupta, Hinrich Schütze

* NAACL 2016

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Ensemble of Convolutional Neural Networks Trained with Different Activation Functions

May 31, 2019

Gianluca Maguolo, Loris Nanni, Stefano Ghidoni

May 31, 2019

Gianluca Maguolo, Loris Nanni, Stefano Ghidoni

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Extension of Convolutional Neural Network with General Image Processing Kernels

Jan 16, 2019

Jay Hoon Jung, Yousun Shin, YoungMin Kwon

Jan 16, 2019

Jay Hoon Jung, Yousun Shin, YoungMin Kwon

* TENCON 2018

* 4 pages, 6 figures

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Generalizing the Convolution Operator in Convolutional Neural Networks

Jul 14, 2017

Kamaledin Ghiasi-Shirazi

Jul 14, 2017

Kamaledin Ghiasi-Shirazi

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Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. The involved deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks having convolutional structures. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.

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Kernel-based Translations of Convolutional Networks

Mar 19, 2019

Corinne Jones, Vincent Roulet, Zaid Harchaoui

Mar 19, 2019

Corinne Jones, Vincent Roulet, Zaid Harchaoui

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Bayesian Convolutional Neural Networks

Sep 10, 2018

Kumar Shridhar, Felix Laumann, Adrian Llopart Maurin, Marcus Liwicki

Sep 10, 2018

Kumar Shridhar, Felix Laumann, Adrian Llopart Maurin, Marcus Liwicki

* arXiv admin note: text overlap with arXiv:1704.02798 by other authors

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