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Operator learning's zero-shot super-resolution gains theoretical grounding

By PulseAugur Editorial · [2 sources] ·

Researchers have theoretically investigated the phenomenon of zero-shot super-resolution in operator learning, where models trained on coarse grids can predict on finer grids without retraining. The study reveals that this capability can be information-theoretically impossible in certain benign scenarios, such as with rank-one linear operators. However, the research identifies H"older smoothness of output functions as a sufficient condition for successful zero-shot super-resolution and provides corresponding generalization bounds. AI

IMPACT Provides theoretical understanding for a key capability in operator learning models.

RANK_REASON The cluster contains an academic paper discussing theoretical aspects of a machine learning phenomenon.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Unique Subedi, Ambuj Tewari ·

    Is Zero-Shot Super-Resolution Possible in Operator Learning?

    arXiv:2606.00296v1 Announce Type: new Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empi…

  2. arXiv stat.ML TIER_1 English(EN) · Ambuj Tewari ·

    Is Zero-Shot Super-Resolution Possible in Operator Learning?

    Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empirical evidence, the theoretical foundations of t…

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