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University of Florence
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Planted attractors in Neural-ODEs can transform classification tasks by directing inputs to their target classes through dynamically shaped velocity fields.
Higher-order neural networks don't need hypergraphs: SHONNs unlock their power for general-purpose feedforward architectures by sidestepping stability and scaling issues.
