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Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces
Research article (Foundations of Computational Mathematics, 2023) · cited 35× · AI/ML
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces
Summary
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces is a scholarly article[1].
Key Facts
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces. Retrieved May 24, 2026, from https://4ort.xyz/entity/proof-of-the-theory-to-practice-gap-in-deep-learning-via-sampling-complexity-bounds-for-neural-network-approximation-spa
MLA“Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/proof-of-the-theory-to-practice-gap-in-deep-learning-via-sampling-complexity-bounds-for-neural-network-approximation-spa.
BibTeX@misc{4ortxyz_proof-of-the-theory-to-practice-gap-in-deep-learning-via-sampling-complexity-bounds-for-neural-network-approximation-spa_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces}}, year = {2026}, url = {https://4ort.xyz/entity/proof-of-the-theory-to-practice-gap-in-deep-learning-via-sampling-complexity-bounds-for-neural-network-approximation-spa}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces — https://4ort.xyz/entity/proof-of-the-theory-to-practice-gap-in-deep-learning-via-sampling-complexity-bounds-for-neural-network-approximation-spa (retrieved 2026-05-24)
