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يعرض 1 - 20 نتائج من 63 نتيجة بحث عن '(( significant ((aspect decrease) OR (largest decrease)) ) OR ( significant attention layer ))', وقت الاستعلام: 0.11s تنقيح النتائج
  1. 1

    Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural Networks حسب Ahmed , Abdelrahman

    منشور في 2020
    "…We explore several designs for the classifier based on convolutional neural networks (CNNs), long-short-term memory networks (LSTMs), and an attention layer. The corpus consists of seven hours collected and annotated from three different call centers. …"
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  2. 2

    Efficient self-attention with smart pruning for sustainable large language models حسب Samir Brahim Belhaouari (9427347)

    منشور في 2025
    "…Second, the Weight Matrix Folding method is introduced to efficiently prune the self-attention layer matrices in a simple and efficient mathematical model. …"
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    An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques حسب Fariha Haque (21485518)

    منشور في 2025
    "…This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. …"
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    Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis حسب Najmath, Ottakath

    منشور في 2024
    "…This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. …"
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    Bi-attention DoubleUNet: A deep learning approach for carotid artery segmentation in transverse view images for non-invasive stenosis diagnosis حسب Najmath Ottakath (17430912)

    منشور في 2024
    "…This paper proposes an automated segmentation method for the carotid artery in transverse B-mode ultrasound images, using a Bi-attention DoubleUnet architecture which incorporates spatial attention and channel wise attention using Bottleneck attention module. …"
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    FLACON: A Deep Federated Transfer Learning-Enabled Transient Stability Assessment During Symmetrical and Asymmetrical Grid Faults حسب Mohamed Massaoudi (16888710)

    منشور في 2024
    "…By introducing convolutional layers alongside multi-head attention mechanisms, the FLACON framework significantly improves learning efficiency across geographically distributed datasets. …"
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