AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The method, adVersarial Parameter Decomposition (VPD), optimizes for decompositions of neural network parameters into simple subcomponents that preserve the network's input-output behavior even when many subcomponents are ablated, including under ablations that are adversarially selected to destroy behavior.
A mathematical theory of superposition in neural networks using tools from frame theory and compressed sensing and a novel characterization of the distribution of signs in the Gram matrix is developed.
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