SOTAVerified

Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 671–680 of 2226 papers

TitleStatusHype
Geometry of Score Based Generative Models—0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetsCode1
Fortuna: A Library for Uncertainty Quantification in Deep LearningCode2
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Quantifying tissue growth, shape and collision via continuum models and Bayesian inferenceCode0
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field—0
Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexityCode0
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity—0
Show:102550
← PrevPage 68 of 223Next →

Benchmark Results

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