Multi-layer perceptrons (MLPs) have become essential components in modern deep learning models, offering versatility in approximating nonlinear functions across various tasks. However, these neural networks face challenges in interpretation and scalability. The difficulty in understanding learned representations limits their transparency, while expanding the network scale often proves complex. Also, MLPs rely on fixed activation functions, […]
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