Results 11 to 20 of about 54,299 (266)
Quantum Dilated Convolutional Neural Networks
IEEE Access, 2022 In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements ...
Yixiong Chen
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
Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System [PDF]
Sensors, 2018 Shin Dug Kim, Kim Shin-Dug
exaly +2 more sources
Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE +3 more
openaire +4 more sources
Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
openaire +3 more sources
Fast 3D-CNN Combined with Depth Separable Convolution for Hyperspectral Image Classification [PDF]
Jisuanji kexue yu tansuo, 2022 In the process of feature extraction and classification of hyperspectral images using convolution neural networks, there are problems such as insufficient extraction of spatial spectrum features and too many layers of networks, which lead to large ...
WANG Yan, LIANG Qi
doaj +1 more source
Selective kernel networks for weakly supervised relation extraction
CAAI Transactions on Intelligence Technology, 2021 The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities. Recently, many variants of the convolution neural network (CNN) have been introduced to relation extraction for the ...
Ziyang Li +4 more
doaj +1 more source
Micromachines, 2023 Memristor crossbars can be very useful for realizing edge-intelligence hardware, because the neural networks implemented by memristor crossbars can save significantly more computing energy and layout area than the conventional CMOS (complementary metal ...
Seokjin Oh, Jiyong An, Kyeong-Sik Min
doaj +1 more source
Rolling bearing intelligent fault diagnosis method based on IPSO-WCNN
Measurement + Control, 2023 In the bearing fault diagnosis process using the convolution neural network (CNN), there are some problems, such as complex signal data processing and the complex network parameter setting.
Ronghua Chen +3 more
doaj +1 more source
Voronoi Convolutional Neural Networks
CoRR, 2020 Technical ...
Soroosh Yazdani, Andrea Tagliasacchi
openaire +2 more sources