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arXiv Machine Learning By Juan Ramirez, Meraj Hashemizadeh, Simon Lacoste-Julien

Position: Adopt Constraints Over Fixed Penalties in Deep Learning

Read the original on arXiv Machine Learning →

The paper argues against using fixed weighted-sum penalties to enforce constraints in deep learning, citing three main issues: non-equivalence of penalized and constrained problems in non-convex settings, difficulty in selecting appropriate penalty coefficients, and the softening of hard requirements. It contends that these problems can lead to non-compliance and relaxed constraints. The authors recommend directly solving the constrained problem when non-negotiable requirements are present.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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