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Benign Loss Landscapes Can Coexist with Worst-Case Hardness

This story is from 2026-09-14. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2609.13057v1 Announce Type: cross Abstract: Deep neural networks are expressive enough to contain worst-case targets that can be evaluated in polynomial time but cannot be learned in polynomial time by gradient descent. For practical tasks they nonetheless learn well, raising the question of…

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  1. 2026-09-14 04:00 · arXiv stat.ML
    Benign Loss Landscapes Can Coexist with Worst-Case Hardness

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