Results 31 to 40 of about 54,299 (266)

Convolutional Neural Networks: A Survey

Computers, 2023
Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of ...
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Canonical convolutional neural networks

2022 International Joint Conference on Neural Networks (IJCNN), 2022
We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode ...
Lokesh Veeramacheneni, Moritz Wolter, Reinhard Klein, Jochen Garcke   +3 more
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FocusedDropout for Convolutional Neural Network

CoRR, 2021
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units.
Tianshu Xie, Minghui Liu, Jiali Deng, Xuan Cheng, Xiaomin Wang, Ming Liu 0002   +5 more
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Winograd Heterogeneous Sampling Window Convolution Acceleration Operator [PDF]

Jisuanji gongcheng
In recent years, Artificial Intelligence (AI) has been widely used in fields such as large models, autonomous driving, and robotics. As the core of AI, neural networks process big data, learn, adapt complex patterns, and perform various tasks.
PENG Yun, WANG Yubing, LIANG Lei, SONG Yue, QIU Cheng, LEI Yuxin, JIA Peng, MIAO Guoqing, QIN Li, WANG Lijun
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Convolutional neural networks in APL [PDF]

Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, 2019
This paper shows how a Convolutional Neural Network (CNN) can be implemented in APL. Its first-class array support ideally fits that domain, and the operations of APL facilitate rapid and concise creation of generically reusable building blocks. For our example, only ten blocks are needed, and they can be expressed as ten lines of native APL. All these
Artjoms Sinkarovs, Robert Bernecky, Sven-Bodo Scholz   +2 more
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reg-sgc: An open-source software for regularized Simple Graph Convolution

SoftwareX, 2023
Attributed graphs are powerful tools to represent real-life systems in many domains such as social networks, biological metabolic networks, consumer recommendation systems and more.
Patrick Pho, Alexander V. Mantzaris
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A Graph-Voxel Joint Convolution Neural Network for ALS Point Cloud Segmentation

IEEE Access, 2020
A deep convolution neural network is frequently used in airborne laser scanning (ALS) point cloud segmentation. In this study, we propose a joint graph-voxel convolution network to recognize on-ground objects accurately.
Jinming Zhang, Xiangyun Hu, Hengming Dai
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Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks

IEEE Access, 2022
Since printed circuit board (PCB) is the key to ensure the reliability of electronic equipment. Therefore, defect detection for PCB is a basic and necessary work.
Jianfeng Zheng, Xiaopeng Sun, Haixiang Zhou, Chenyang Tian, Hao Qiang   +4 more
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Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array

IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020
Oscillator neural networks (ONNs) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date.
Dmitri E. Nikonov, Peter Kurahashi, James S. Ayers, Hai Li, Telesphor Kamgaing, Georgios C. Dogiamis, Hyung-Jin Lee, Yongping Fan, I. A. Young   +8 more
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Aerial Image Semantic Classification Method Based on Improved Full Convolution Neural Network [PDF]

Jisuanji gongcheng, 2017
The existing Convolution Neural Networks(CNNs) method cannot semantically identify each pixel,and it is difficult to decompose the different types of images from the pixel level.Therefore,an end-to-end full-convolution depth network is proposed to ...
YI Meng,SUI Lichun
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