AINeutralarXiv – CS AI · May 126/10
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Improving Generalization by Permutation Routing Across Model Copies
Researchers introduce an M-cover transform method that improves neural network generalization by replicating models and routing learning messages across copies through structured permutations, rather than relying on parameter averaging. The approach applies across different model architectures from perceptrons to multilayer networks, offering a novel mechanism for distributed learning that avoids replica collapse.