MDL-based regularization, which balances data fit with a network's encoding length, preserves perfect solutions on several formal-language tasks, while standard L1, L2, and no regularization degrade them.
On the implicit bias of gradient descent for temporal extrapolation
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A Minimum Description Length Approach to Regularization in Neural Networks
MDL-based regularization, which balances data fit with a network's encoding length, preserves perfect solutions on several formal-language tasks, while standard L1, L2, and no regularization degrade them.