SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

Showing 21012125 of 2226 papers

TitleStatusHype
Likelihood-free inference via classification0
Relevance Singular Vector Machine for low-rank matrix sensing0
Reliable ABC model choice via random forests0
Semi-Supervised Learning with Deep Generative ModelsCode1
Evaluation of Machine Learning Techniques for Green Energy Prediction0
Causal Inference through a Witness Protection Program0
Gaussian Processes for Natural Language Processing0
Non-Parametric Bayesian Constrained Local Models0
Bayesian inference as a cross-linguistic word segmentation strategy: Always learning useful things0
Accelerating MCMC via Parallel Predictive Prefetching0
Approximate Decentralized Bayesian Inference0
Firefly Monte Carlo: Exact MCMC with Subsets of Data0
Particle methods enable fast and simple approximation of Sobolev gradients in image segmentation0
Marginalizing Corrupted Features0
Scaling Nonparametric Bayesian Inference via Subsample-Annealing0
Building fast Bayesian computing machines out of intentionally stochastic, digital parts0
Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification0
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision ModelsCode0
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank DeterminationCode0
Stochastic Backpropagation and Approximate Inference in Deep Generative ModelsCode0
Solving #SAT and Bayesian Inference with Backtracking Search0
Bayesian Conditional Density FilteringCode0
Multiscale Shrinkage and Lévy Processes0
Online Bayesian Passive-Aggressive Learning0
Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks0
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Benchmark Results

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