Results 61 to 70 of about 204,781 (309)
Dual-channel deep graph convolutional neural networks
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
doaj +1 more source
An Introduction to Convolutional Neural Networks
CoRR, 2015 10 pages, 5 ...
Keiron O'Shea, Ryan Nash
openaire +2 more sources
Artificial Neural Networks and Evolutionary Computation in Remote Sensing [PDF]
, 2021 Artificial neural networks (ANNs) and evolutionary computation methods have been successfully applied in remote sensing applications since they offer unique advantages for the analysis of remotely-sensed images.
core +1 more source
Doubly Convolutional Neural Networks
CoRR, 2016 Building large models with parameter sharing accounts for most of the success of deep convolutional neural networks (CNNs). In this paper, we propose doubly convolutional neural networks (DCNNs), which significantly improve the performance of CNNs by further exploring this idea.
Shuangfei Zhai +3 more
openaire +3 more sources
Homological Convolutional Neural Networks
CoRR, 2023 Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still pose a challenge, with classic machine learning approaches being often computationally cheaper and equally effective than increasingly complex deep learning ...
Antonio Briola +3 more
openaire +3 more sources
Symplectic convolutional neural networks
CoRRWe propose a new symplectic convolutional neural network (CNN) architecture by leveraging symplectic neural networks, proper symplectic decomposition, and tensor techniques. Specifically, we first introduce a mathematically equivalent form of the convolution layer and then, using symplectic neural networks, we demonstrate a way to parameterize the ...
Yildiz, S. +2 more
openaire +3 more sources
Morphological convolutional neural network architecture for digit recognition [PDF]
, 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. +4 more
core +1 more source
Classification methods for handwritten digit recognition: A survey
Vojnotehnički Glasnik, 2023 Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit ...
Ira M. Tuba +2 more
doaj +1 more source
astorfi/3D-convolutional-speaker-recognition: 3D Convolutional Neural Networks for Speaker Verification [PDF]
, 2017 <p>This project is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following the SR protocol.</p ...
Amirsina Torfi
core +1 more source
Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
wiley +1 more source