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 1971–1980 of 2226 papers

TitleStatusHype
Connections between sequential Bayesian inference and evolutionary dynamics—0
Consciousness is entailed by compositional learning of new causal structures in deep predictive processing systems—0
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck—0
Constrained Bayesian Inference for Low Rank Multitask Learning—0
Constrained Bayesian Networks: Theory, Optimization, and Applications—0
Constrained belief updates explain geometric structures in transformer representations—0
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks—0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Consumer Demand Modeling During COVID-19 Pandemic—0
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Benchmark Results

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