SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1611–1620 of 2226 papers

TitleStatusHype
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Learning and Inference in Hilbert Space with Quantum Graphical Models—0
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications—0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
Learning Curves for Deep Neural Networks: A field theory perspective—0
Learning Deep Generative Models with Doubly Stochastic MCMC—0
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified