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AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks
Published 2025-01-01“…This study proposes AdaptedNorm, a principled framework that establishes connections between normalization schemes and GCN task objectives, while introducing three key contributions: 1) A novel random walk re-normalized transformation (RWRT) technique for graph representation normalization; 2) A task-aligned modeling paradigm demonstrating that symmetric re-normalized transformation (SRT) enhances node classification accuracy, while RWRT achieves superior performance in link prediction; 3) Integration of normalization schemes with GNN sparsification strategies, enabling effective model compression without sacrificing performance. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis
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Customized Fetal Body Mass Index as a Better Predictive Marker for Neonatal Nutritional Status
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Using nonlinear auto-regressive with exogenous input neural network (NNARX) in blood glucose prediction
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Mapping predicted ecological states at landscape scales using remote‐sensing data and machine learning
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Low-cycle fatigue life prediction method for stud connectors based on interpretable machine learning
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Designing a model for re-employment of employees during retirement (Study case: Ministry of Education)
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