A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

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Quantization-Aware Training With Dynamic and Static Pruning by Sangho An, Jongyun Shin, Jangho Kim

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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

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VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation by Guiwen Xu, Tao Hu, Qinghua Zhang

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Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting by Lifeng Gao, Liujia Chen, Agen Qiu, Qinglian Wang, Jianlong Wang, Cai Chen, Fuhao Zhang, Geli Ou’er

“…Therefore, a novel grid partition-based dynamic spatial–temporal graph convolutional network was developed in this study to capture correlations within a large-scale traffic network. …”
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Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation by Quentin Malé, Corentin J. Lapeyre, Nicolas Noiray

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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

Subjects: “…convolutional neural network…”
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Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification by Xuebin Tang, Ke Zhang, Xiaolei Zhou, Lingbin Zeng, Shan Huang

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Disentangling Regional Drivers of Top Antarctic Snowfall Days With a Convolutional Neural Network by Rebecca Baiman, Andrew C. Winters, Kirsten J. Mayer, Clairisse A. Reiher

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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. …”
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Attention-Based Convolutional Aggregation: An Efficient Model for Off-Gas Profile Forecasting and Dynamic Pre-Control of BOF Steelmaking by Tian-yi Xie, Jun-guo Zhang, Lan-jie Li, Fei Zhang, Shuai Liu, Han-jie Guo

“…Then, a deep-learning model is proposed to forecast the dynamic off-gas profile, named attention-based convolutional aggregation (ABCA). …”
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EOST-LSTM: Long Short-Term Memory Model Combined with Attention Module and Full-Dimensional Dynamic Convolution Module by Guangxin He, Wei Wu, Jing Han, Jingjia Luo, Lei Lei

“…The full-dimensional dynamic convolutional module introduces the dynamic attention mechanism in the spatial position and input and output channels of the convolutional kernel, adaptively adjusts the weight of the convolutional kernel, and improves the flexibility and efficiency of feature extraction. …”
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Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system by Philipp Teutsch, Philipp Pfeffer, Mohammad Sharifi Ghazijahani, Christian Cierpka, Jörg Schumacher, Patrick Mäder

“…In recent years, data-driven deep learning models have gained significant importance in the analysis of turbulent dynamical systems. Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. …”
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Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…In this article, uneven-aged Chinese fir (<italic>Cunninghamia lanceolata</italic>) plantations were chosen as our study subject and proposed a novel method of forest dynamic growth visualization modeling by incorporating spatial structure parameters and using convolutional neural network technique (FDGVM-CNN-SSP) to explore the effect of spatial structure on the morphological growth and to develop a prediction growth model of Chinese fir plantations by introducing a convolutional neural network (CNN) model. …”
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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land by Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang

“…Subsequently, a novel model of edge-enhanced dynamic graph convolutional network (GCN) (EDG-Net) was proposed to learn the discriminative features for classification of mining land with irregular edges, different sizes, a relatively small proportion, and sparse spatial distribution. (1) Edge-enhanced multiscale attention module: it is designed to capture key multiscale features and edge details using parallel dilated convolutions with attention fusion and edge enhancement, which facilitates the identification of objects with irregular edges and different sizes. (2) Downsampling fusion module: it integrates the features obtained through spatially split learning and max-pooling to overcome the information loss issue of small objects. (3) Patch-based dynamic GCN: the input images were split into several patches as nodes, and a graph was constructed and dynamically updated by connecting the nearest neighbors. …”
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Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction by Juan Chen, Rui Huang

“…The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. …”
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ADAPTATION OF THE BACK PROPAGATION ERROR ALGORITHM FOR A CONVOLUTIONAL NEURAL NETWORK WITH SECOND-ORDER NEURONS AND DYNAMIC RECEPTIVE FIELDS by Roman Mikhailovich Nemkov, Oksana Stanislavovna Mezentseva, Dmitriy Viktorovich Mezentsev

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EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Methods: To address these challenges, this paper proposes a multi-dimensional attention-based dynamic graph convolutional neural network (AttGraph) model. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

Subjects: “…Dynamic 3-D convolution…”
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