AIMC Topic: Attention

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Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
Neural Networks, Computer Algorithms Electroencephalography Signal Processing, Computer-Assisted Deep Learning Signal-To-Noise Ratio Humans Imagination Attention Brain-Computer Interfaces
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A Static Sign Language Recognition Method Enhanced with Self-Attention Mechanisms.

Sensors (Basel, Switzerland) Oct 29, 2024
For the current wearable devices in the application of cross-diversified user groups, it is common to face the technical difficulties of static sign language recognition accuracy attenuation, weak anti-noise ability, and insufficient system robustnes...
Gestures Algorithms Humans Attention Pattern Recognition, Automated Sign Language Neural Networks, Computer Wearable Electronic Devices
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Personalized multi-head self-attention network for news recommendation.

Neural networks : the official journal of the International Neural Network Society Oct 22, 2024
With the rapid explosion of online news and user population, personalized news recommender systems have proved to be efficient ways of alleviating information overload problems by suggesting information which attracts users in line with their tastes....
Humans Neural Networks, Computer Attention
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Hypergraph contrastive attention networks for hyperedge prediction with negative samples evaluation.

Neural networks : the official journal of the International Neural Network Society Oct 19, 2024
Hyperedge prediction aims to predict common relations among multiple nodes that will occur in the future or remain undiscovered in the current hypergraph. It is traditionally modeled as a classification task, which performs hypergraph feature learnin...
Neural Networks, Computer Algorithms Reproducibility of Results Humans Attention
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Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

Computers in biology and medicine Oct 18, 2024
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
Imagination Attention Brain-Computer Interfaces Neural Networks, Computer Brain Electroencephalography Signal Processing, Computer-Assisted Humans Deep Learning
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Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection.

Neural networks : the official journal of the International Neural Network Society Oct 11, 2024
Dictionary representations and deep learning Autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture...
Neural Networks, Computer Image Processing, Computer-Assisted Attention Hyperspectral Imaging Algorithms Deep Learning
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Classification of Internal and External Distractions in an Educational VR Environment Using Multimodal Features.

IEEE transactions on visualization and computer graphics Oct 11, 2024
Virtual reality (VR) can potentially enhance student engagement and memory retention in the classroom. However, distraction among participants in a VR-based classroom is a significant concern. Several factors, including mind wandering, external noise...
Brain Computer Graphics Male Electroencephalography Eye-Tracking Technology Humans Attention Virtual Reality Young Adult Neural Networks, Computer Female Adult Machine Learning
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ExGAT: Context extended graph attention neural network.

Neural networks : the official journal of the International Neural Network Society Oct 5, 2024
As an essential concept in attention, context defines the overall scope under consideration. In attention-based GNNs, context becomes the set of representation nodes of graph embedding. Current approaches choose immediate neighbors of the target or i...
Computer Simulation Neural Networks, Computer Attention Algorithms Humans
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BAB-GSL: Using Bayesian influence with attention mechanism to optimize graph structure in basic views.

Neural networks : the official journal of the International Neural Network Society Oct 5, 2024
In recent years, Graph Neural Networks (GNNs) have garnered significant attention, with a notable focus on Graph Structure Learning (GSL), a branch dedicated to optimizing graph structures to enhance network training performance. Current GSL methods ...
Neural Networks, Computer Attention Machine Learning Humans Computer Graphics Algorithms Bayes Theorem
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ECG classification based on guided attention mechanism.

Computer methods and programs in biomedicine Oct 3, 2024
BACKGROUND AND OBJECTIVE: Integrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specif...
Atrial Fibrillation Wolff-Parkinson-White Syndrome Algorithms Deep Learning Humans Signal Processing, Computer-Assisted Electrocardiography Attention
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