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Per-object systematics using deep-learned calibration

Research article (SciPost Physics, 2020) · cited 43× · AI/ML
academia openalex:W3013695373
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Per-object systematics using deep-learned calibration

Summary

Per-object systematics using deep-learned calibration is a scholarly article[1].

Key Facts

  • Per-object systematics using deep-learned calibration's instance of is recorded as scholarly article[2].

Related Entities

Gregor Kasieczka

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Per-object systematics using deep-learned calibration. Retrieved May 24, 2026, from https://4ort.xyz/entity/per-object-systematics-using-deep-learned-calibration
MLA “Per-object systematics using deep-learned calibration.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/per-object-systematics-using-deep-learned-calibration.
BibTeX @misc{4ortxyz_per-object-systematics-using-deep-learned-calibration_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Per-object systematics using deep-learned calibration}}, year = {2026}, url = {https://4ort.xyz/entity/per-object-systematics-using-deep-learned-calibration}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Per-object systematics using deep-learned calibration — https://4ort.xyz/entity/per-object-systematics-using-deep-learned-calibration (retrieved 2026-05-24)

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