MallaNet’s novelty lies in its tailored architecture, combining optimized residual blocks and HFC layers to capture Devanagari’s intricate features with 17 million parameters, achieving a test ...
A research team led by Prof. Gao Xiaoming from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has improved residual neural networks to accurately classify and identify ...
Physics-informed convolutional neural networks (PICNNs) have emerged as a powerful extension of physics-informed neural networks (PINNs), offering superior generalization and efficiency for solving ...
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