Home › Entities › academia › Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.

Research article (arXiv (Cornell University), 2019) · cited 22× · AI/ML
academia openalex:W2931933838
Press Enter · cited answer in seconds

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.

Summary

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. is a scholarly article[1].

Key Facts

  • Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.'s instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.. Retrieved May 24, 2026, from https://4ort.xyz/entity/quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition
MLA “Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition..” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition.
BibTeX @misc{4ortxyz_quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition.}}, year = {2026}, url = {https://4ort.xyz/entity/quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. — https://4ort.xyz/entity/quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/quantifying-interpretability-of-arbitrary-machine-learning-models-through-functional-decomposition · Last refreshed: