Deep learning research advocates for constraints over fixed penalties

This paper argues that fixed penalization is an inadequate method for handling constraints in deep learning. The authors contend that this approach, commonly used for trustworthy AI, often fails to solve the intended constrained problem due to non-convexity and the trade-off between requirements and performance. They advocate for starting with the constrained formulation directly rather than relying on surrogate penalized objectives, especially when requirements are non-negotiable. AI

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IMPACT Suggests a more robust theoretical framework for building trustworthy AI systems by directly addressing constraints.

RANK_REASON This is a research paper published on arXiv discussing a theoretical approach to deep learning constraints. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Meraj Hashemizadeh

• paper
• safety