Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.
To ensure a fair comparison under equal computational budgets, we additionally include a 35-member Deep Ensemble—the most performant competing method
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
Sampling-based inference for Bayesian neural networks has achieved computational parity with optimization-based methods and should be prioritized to deliver better uncertainty quantification and model insights.