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 691–700 of 2226 papers

TitleStatusHype
A majorization-minimization algorithm for nonnegative binary matrix factorization—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
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
Constraining subglacial processes from surface velocity observations using surrogate-based Bayesian inference—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
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Benchmark Results

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