Hypernetworks distill modular reservoir connectivity via a genomic bottleneck to generate sparse recurrent networks solving difficult temporal tasks with minimal training and maintained robustness.
Proceedings of the National Academy of Sciences , volume=
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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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Distilling a Modular Reservoir Through a Genomic Bottleneck
Hypernetworks distill modular reservoir connectivity via a genomic bottleneck to generate sparse recurrent networks solving difficult temporal tasks with minimal training and maintained robustness.
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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.