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 1701–1710 of 2226 papers

TitleStatusHype
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Bandit Learning with Implicit FeedbackCode0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Stochastic Gradient MCMC with Repulsive ForcesCode0
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting—0
Uncertainty propagation in neural networks for sparse coding—0
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference—0
Amortized Bayesian inference for clustering modelsCode0
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Benchmark Results

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