Unifying Neural Network Design with Category Theory: A Comprehensive Framework for Deep Learning Architecture

In deep learning, a unifying framework to design neural network architectures has been a challenge and a focal point of recent research. Earlier models have been described by the constraints they must satisfy or the sequence of operations they perform. This dual approach, while useful, has lacked a cohesive framework to integrate both perspectives seamlessly.  […]

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