A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A new supervised classification method is built on Minimum Spanning Trees, with a robust efficient version tested in simulations and on aircraft trajectory data.
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Architecture Generalization with MetaNCA
A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
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A new classification method based on Minimum Spanning Trees
A new supervised classification method is built on Minimum Spanning Trees, with a robust efficient version tested in simulations and on aircraft trajectory data.