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 1761–1770 of 2226 papers

TitleStatusHype
Large-Scale Stochastic Sampling from the Probability SimplexCode0
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning—0
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational InferenceCode0
Reconstructing networks with unknown and heterogeneous errors—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-DivergencesCode0
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference—0
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing—0
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filterCode0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Wasserstein Variational Inference—0
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Benchmark Results

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