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New Theory Explains Neural Scaling Laws in Operator Learning

By PulseAugur Editorial · [1 sources] ·

This paper presents a theoretical framework for understanding neural scaling laws in deep operator networks, specifically focusing on architectures like DeepONet. The study analyzes approximation and generalization errors in relation to network size and training data, offering tighter bounds for inputs with low-dimensional structures. These findings also extend to deep ReLU networks, providing a theoretical basis for operator learning. AI

RANK_REASON This is a theoretical study published on arXiv concerning neural scaling laws in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Theory Explains Neural Scaling Laws in Operator Learning

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This is a theoretical study published on arXiv concerning neural scaling laws in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer ·

    Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

    arXiv:2410.00357v2 Announce Type: replace-cross Abstract: Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains und…

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