Results 91 to 100 of about 3,882,996 (203)

An Embedded Inference Framework for Convolutional Neural Network Applications

IEEE Access, 2019
With the rapid development of deep convolutional neural networks, more and more computer vision tasks have been well resolved. These convolutional neural network solutions rely heavily on the performance of the hardware. However, due to privacy issues or
Sheng Bi, Yingjie Zhang, Min Dong, Huaqing Min   +3 more
doaj   +1 more source

Hybrid multi-objective evolutionary model compression with convolutional neural networks

Results in Engineering
Deep learning has been utilized in the fields of image processing, natural language processing and speech recognition. For improving the structure of deep learning, how to compress Convolutional Neural Networks has become a major focus topic.
Shuhan Zhang, Yanjie Gao
doaj   +1 more source

Multi-Level Wavelet Convolutional Neural Networks

IEEE Access, 2019
In computer vision, convolutional networks (CNNs) often adopt pooling to enlarge receptive field which has the advantage of low computational complexity.
Pengju Liu, Hongzhi Zhang, Wei Lian, Wangmeng Zuo   +3 more
doaj   +1 more source

Fine Tuning Hyperparameters of Deep Learning Models Using Metaheuristic Accelerated Particle Swarm Optimization Algorithm

IEEE Access
In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing.
Abdel-Hamid M. Emara, Ghada Atteia, Jawad Hasan Alkhateeb   +2 more
doaj   +1 more source

Morphological convolutional neural network architecture for digit recognition

, 2019
Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper
Hamdani, Tarek M., Ayed, Mounir Ben, Sanchez-Medina, Javier J., Mellouli, Dorra, Alimi, Adel M.   +4 more
core   +1 more source

Structured learning via convolutional neural networks for vehicle detection

, 2017
One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches.
Ana I. Maqueda, García Santos, Narciso, Jaureguizar Núñez, Fernando, Carlos R. del Blanco, Fernando Jaureguizar, Maqueda Nieto, Ana Isabel, Blanco Adán, Carlos Roberto del, Narciso García   +7 more
core   +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

Understanding convolutional neural networks: theory and applications

, 2021
We focus on the theory of convolutional neural networks. Starting with a bit of history, then explaining its components. We mention some structures that played an important role throughout deep learning history and why convolutional neural networks are ...
Zhu, Jieying
core  

Convolutional Neural Networks

, 2018
This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are presently widely used mainly for image recognition and natural language processing.
Lietavcová, Zuzana
core  

Data from: The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference

, 2018
Each file is a numpy "npz" file. More details here: https://docs.scipy.org/doc/numpy/reference/generated/numpy.savez.html And contains separate test, validate, and training data sets, for both the input data and response.Large neural network training ...
Flagel, Lex, Brandvain, Yaniv, Schrider, Daniel   +2 more
core   +1 more source