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 1531–1540 of 2226 papers

TitleStatusHype
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees—0
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Implicit Causal Models for Genome-wide Association Studies—0
Implicit Full Waveform Inversion with Deep Neural Representation—0
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Benchmark Results

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