Historical milestone · 2017
On Calibration of Modern Neural Networks
Reviewed through September 18, 2026
2017 · Historical milestone
On Calibration of Modern Neural Networks
- Era
- 2010s
- Theme
- Reliability, uncertainty & evaluation
- Evidence form
- Benchmark study
- School / paradigm
- Statistical evaluation / uncertainty
- Institution / context
- Cornell University
- Researchers
- Chuan Guo; Geoff Pleiss; Yu Sun; Kilian Weinberger
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Measured confidence against observed correctness across modern neural architectures and evaluated post-hoc calibration methods.
Result / historical claim. Showed that accuracy gains did not imply calibrated probabilities and found temperature scaling effective in tested settings.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: Reliability, uncertainty, and evaluation.
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Benchmark study
Limitation / debate. Held-out in-distribution calibration does not guarantee calibration after distribution shift.
Source status. The source link below is the verified link our reviewed topic research already carries for this milestone.
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A reproduction tutorial is not yet available for this entry. The closest reviewed material is Reliability, uncertainty, and evaluation.
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.