Results 31 to 40 of about 52,085 (312)

High‐precision skeleton‐based human repetitive action counting

IET Computer Vision, 2023
A novel counting model is presented by the authors to estimate the number of repetitive actions in temporal 3D skeleton data. As per the authors’ knowledge, this is the first work of this kind using skeleton data for high‐precision repetitive action ...
Chengxian Li, Ming Shao, Qirui Yang, Siyu Xia   +3 more
doaj   +1 more source

BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks [PDF]

IEEE Access, 2019
Binarization can greatly compress and accelerate deep convolutional neural networks (CNNs) for real-time industrial applications. However, existing binarized CNNs (BCNNs) rely on scaling factor (SF) and batch normalization (BatchNorm) that still involve resource-consuming floating-point multiplication operations.
Lijun Wu, Peiqing Jiang, Zhicong Chen, Xu Lin, Yunfeng Lai, Peijie Lin, Shuying Cheng   +6 more
openaire   +2 more sources

Multi‐objective single‐shot neural architecture search via efficient convolutional filters

Electronics Letters, 2023
This paper presents a novel approach for fast neural architecture search (NAS) in Convolutional Neural Networks (CNNs) for end‐to‐end License Plate Recognition (LPR).
Seyed Mahdi Shariatzadeh, Mahmood Fathy, Reza Berangi   +2 more
doaj   +1 more source

Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks [PDF]

Communications in Computational Physics, 2020
Deep networks, especially convolutional neural networks (CNNs), have been successfully applied in various areas of machine learning as well as to challenging problems in other scientific and engineering fields. This paper introduces Butterfly-net, a low-complexity CNN with structured and sparse cross-channel connections, together with a Butterfly ...
Li, Y, Cheng, X, Lu, J
openaire   +2 more sources

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation

BMC Medical Informatics and Decision Making, 2023
AbstractBackgroundKidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery.
Peng Sun, Zengnan Mo, Fangrong Hu, Xin Song, Taiping Mo, Bonan Yu, Yewei Zhang, Zhencheng Chen   +7 more
openaire   +3 more sources

Neural waves and computation in a neural net model I: Convolutional hierarchies

Journal of Computational Neuroscience, 2023
Abstract The computational resources of a neuromorphic network model introduced earlier are investigated in the context of such hierarchical systems as the mammalian visual cortex. It is argued that a form of ubiquitous spontaneous local convolution, driven by spontaneously arising wave-like activity---which itself promotes local ...
openaire   +2 more sources

Network Inversion of Convolutional Neural Nets (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence
Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios.
Pirzada Suhail, Amit Sethi
openalex   +3 more sources

Brain inspired neuronal silencing mechanism to enable reliable sequence identification

Scientific Reports, 2022
Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes.
Shiri Hodassman, Yuval Meir, Karin Kisos, Itamar Ben-Noam, Yael Tugendhaft, Amir Goldental, Roni Vardi, Ido Kanter   +7 more
doaj   +1 more source

Novel multi‐domain attention for abstractive summarisation

CAAI Transactions on Intelligence Technology, 2023
The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary, and the summary generated by models lacks the cover of the subject of source document due to models ...
Chunxia Qu, Ling Lu, Aijuan Wang, Wu Yang, Yinong Chen   +4 more
doaj   +1 more source

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting

IET Generation, Transmission & Distribution, 2023
Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information.
Shiyan Yi, Haichun Liu, Tao Chen, Jianwen Zhang, Yibo Fan   +4 more
doaj   +1 more source