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 1111–1120 of 2226 papers

TitleStatusHype
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Privacy Amplification Via Bernoulli Sampling—0
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics—0
Improved Neuronal Ensemble Inference with Generative Model and MCMC—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Parametrization invariant interpretation of priors and posteriors—0
Bayesian reconstruction of memories stored in neural networks from their connectivityCode0
Attentional Prototype Inference for Few-Shot SegmentationCode0
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Benchmark Results

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