Results 51 to 60 of about 1,429,068 (340)

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization [PDF]

Conference on Empirical Methods in Natural Language Processing, 2018
We introduce “extreme summarization”, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach.
Shashi Narayan, Shay B. Cohen, Mirella Lapata   +2 more
semanticscholar   +1 more source

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Advanced Materials Interfaces, EarlyView., 2023
This review discusses the use of Surface‐Enhanced Raman Spectroscopy (SERS) combined with Artificial Intelligence (AI) for detecting antimicrobial resistance (AMR). Various SERS studies used with AI techniques, including machine learning and deep learning, are analyzed for their advantages and limitations.
Zakarya Al‐Shaebi, Munevver Akdeniz, Awel Olsido Ahmed, Mine Altunbek, Omer Aydin   +4 more
wiley   +1 more source

A survey of Convolutional Neural Networks —From software to hardware and the applications in measurement

Measurement: Sensors, 2021
The convolutional neural network is a subfield of artificial neural networks and has made great achievements in various domains over the past decade.
Hengyi Li, Xuebin Yue, Zhichen Wang, Wenwen Wang, Hiroyuki Tomiyama, Lin Meng   +5 more
doaj  

Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The 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, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 more
doaj   +1 more source

An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks

IEEE Access
Recent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks.
Li Hai, Chen Liang, Hao Yaming, Yu Wenli, Shi Fengquan   +4 more
doaj   +1 more source

INTELLIGENT COMPUTER VISION SYSTEM FOR UNMANNED AERIAL VEHICLES FOR MONITORING TECHNOLOGICAL OBJECTS OF OIL AND GAS INDUSTRY

Известия Томского политехнического университета: Инжиниринг георесурсов, 2019
The relevance of the research is caused by the necessity to develop modern computer vision systems for monitoring hazardous technological objects of oil and gas industry.
Ivan V. Zoev, Nikolay G. Markov, Svetlana E. Ryzhova   +2 more
doaj   +1 more source

Geometric Deep Learning for Protein–Protein Interaction Predictions

IEEE Access, 2022
This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux, Eric Paquet, Herna L Viktor, Wojtek Michalowski   +3 more
doaj   +1 more source

Non-local Neural Networks [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies.
X. Wang, Ross B. Girshick, A. Gupta, Kaiming He   +3 more
semanticscholar   +1 more source

Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions

Computation, 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.
Mohammad Mustafa Taye
doaj   +1 more source

Spike buffer: improve deep network performance by offset mechanism

The Journal of Engineering, 2020
For a well-designed neural network model, it is difficult to further improve its performance. This study proposes an offset mechanism called spike buffer, which can effectively improve the performance of the designed convolutional neural networks.
Daihui Li, Shangyou Zeng, Chengxu Ma
doaj   +1 more source