Results 61 to 70 of about 1,718,101 (355)
Normalized Convolutional Neural Network
CoRR, 2020 We introduce a Normalized Convolutional Neural Layer, a novel approach to normalization in convolutional networks. Unlike conventional methods, this layer normalizes the rows of the im2col matrix during convolution, making it inherently adaptive to sliced inputs and better aligned with kernel structures. This distinctive approach differentiates it from
Dongsuk Kim +4 more
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SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images [PDF]
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification.
Akbari, Vahid +5 more
core +1 more source
Chemically Inspired Convolutional Neural Network using Electronic Structure Representation
, 2023 In recent years, a development of appropriate crystal representations for accurate prediction of inorganic crystal properties has been considered as one of the essential tasks to accelerate materials discovery through high-throughput virtual screening ...
Seoin, Back +3 more
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ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM
Jixie qiangdu, 2021 Aiming at the problem that it is difficult to extract subtle fault features in the process of rolling bearing fault identification,this paper proposes a rolling bearing fault diagnosis method based on fusion convolutional neural network and support ...
WANG YongDing, JIN ZiQi
doaj
Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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De Computis, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
M. Taye
semanticscholar +1 more source
, 2021 Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network ...
Jiebo, Li +3 more
core +1 more source
Information and Telecommunication Sciences, 2023 Background. The scientists have built effective convolutional neural networks in their research, but the issue of optimal setting of the hyperparameters of these neural networks remains insufficiently researched.
Дмитро Прочухан
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A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection [PDF]
European Conference on Computer Vision, 2016 A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network.
Zhaowei Cai +3 more
semanticscholar +1 more source
An Introduction to Convolutional Neural Networks
CoRR, 2015 10 pages, 5 ...
Keiron O'Shea, Ryan Nash
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