AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. Weight-space symmetries such as neuron permutations make equivariance a key design principle. However, existing equivariant weight-space arch...
This work parameterize a flow-matching velocity field with a permutation-equivariant Graph Meta Network, enabling direct learning from independently trained networks without alignment, and shows how permutation equivariance enables learning from diverse collections of independently trained networks without permutation...
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