Historical milestone · 2017
Simple and scalable predictive uncertainty estimation using deep ensembles
Reviewed through September 18, 2026
2017 · Historical milestone
Simple and scalable predictive uncertainty estimation using deep ensembles
- Era
- 2010s
- Theme
- Reliability, uncertainty & evaluation
- Evidence form
- Benchmark experiment
- School / paradigm
- Ensemble learning / uncertainty
- Institution / context
- Google DeepMind
- Researchers
- Balaji Lakshminarayanan; Alexander Pritzel; Charles Blundell
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Trained independent probabilistic neural networks and aggregated their predictive distributions.
Result / historical claim. Provided a simple strong baseline for uncertainty and out-of-distribution behavior in tested tasks.
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: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Benchmark experiment
Limitation / debate. Multiplies training and inference cost and offers no guarantee under arbitrary distribution shift.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
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Cite or share
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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.