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]
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