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This paper investigates why standard neural architectures fail to generalize to longer inputs in dynamic programming, using geometric analysis with tropical semiring theory to reveal structural limitations in compositions.
This paper establishes an exact correspondence between neural network training and Hamilton-Jacobi initial-value problems, unifying deep learning architectures through a deformation parameter.