Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness is a scholarly article[1].
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APA4ort.xyz Knowledge Graph. (2026). Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Retrieved October 18, 2026, from https://4ort.xyz/entity/quantifying-the-generalization-error-in-deep-learning-in-terms-of-data-distribution-and-neural-network-smoothness
MLA“Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness.” 4ort.xyz Knowledge Graph, 4ort.xyz, 18 Oct. 2026, https://4ort.xyz/entity/quantifying-the-generalization-error-in-deep-learning-in-terms-of-data-distribution-and-neural-network-smoothness.
BibTeX@misc{4ortxyz_quantifying-the-generalization-error-in-deep-learning-in-terms-of-data-distribution-and-neural-network-smoothness_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness}}, year = {2026}, url = {https://4ort.xyz/entity/quantifying-the-generalization-error-in-deep-learning-in-terms-of-data-distribution-and-neural-network-smoothness}, note = {Accessed: 2026-10-18}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness — https://4ort.xyz/entity/quantifying-the-generalization-error-in-deep-learning-in-terms-of-data-distribution-and-neural-network-smoothness (retrieved 2026-10-18)
