Results 1 to 10 of about 5,349,001 (214)

Hardware implementation of a convolutional neural network using calculations in the residue number system [PDF]

Компьютерная оптика, 2019
Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application.
Nikolay Chervyakov, Pavel Lyakhov, Nikolay Nagornov, Maria Valueva, Georgiy Valuev   +4 more
doaj   +1 more source

Voronoi Convolutional Neural Networks

CoRR, 2020
Technical ...
Soroosh Yazdani, Andrea Tagliasacchi
openaire   +3 more sources

Blind Interleaver Recognition Using Deep Learning Techniques

IEEE Access
In digital communication systems, channel encoders and interleavers play a crucial role in mitigating the random and burst errors introduced by noisy channels.
Nayim Ahamed, Swaminathan R., B. Naveen
doaj   +1 more source

Irregular Convolutional Neural Networks [PDF]

2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), 2017
7 pages, 5 figures, 3 ...
Jiabin Ma, Wei Wang 0115, Liang Wang 0001   +2 more
openaire   +4 more sources

Recognition of Internal Overvoltage in Distribution Network Based on Convolutional Neural Network

Electrica, 2022
Fei Long, Huan Xu, Wei Zhan, Yixi Wang, Chengcheng Zou, Jyoti Bhola   +5 more
doaj   +1 more source

Convolutional Graph Neural Networks

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro   +3 more
openaire   +4 more sources

Research on Medical Data Feature Extraction and Intelligent Recognition Technology Based on Convolutional Neural Network

IEEE Access, 2019
In order to mine information from medical health data and develop intelligent application-related issues, the multi-modal medical health data feature representation learning related content was studied, and several feature learning models were proposed ...
Weidong Liu, Caixia Qin, Kun Gao, Heng Li, Zuen Qin, Yafei Cao, Wen Si   +6 more
doaj   +1 more source

Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase

CT Lilun yu yingyong yanjiu
Identifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary
Tianzhe WANG, Wanji ZHANG, Shanbo QI, Guoming JIANG   +3 more
doaj   +1 more source

Diffusion-Convolutional Neural Networks

, 2015
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including
James Atwood, Don Towsley
openaire   +3 more sources

Compressing Convolutional Neural Networks

CoRR, 2015
Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers.
Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen 0001   +4 more
openaire   +3 more sources