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 776–800 of 2226 papers

TitleStatusHype
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
A rational decision making framework for inhibitory control—0
Accounting for contact network uncertainty in epidemic inferences—0
Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration—0
Bayesian Interpolation with Deep Linear Networks—0
Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition—0
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs—0
A Probabilistic Semi-Supervised Approach with Triplet Markov Chains—0
Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning—0
A Probabilistic Model of Social Decision Making based on Reward Maximization—0
Bayesian Inference with Generative Adversarial Network Priors—0
Bayesian inference with finitely wide neural networks—0
A Probabilistic Model for Canonicalizing Named Entity Mentions—0
A Formal Calculus for International Relations Computation and Evaluation—0
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics—0
A Bayesian Approach for Accurate Classification-Based Aggregates—0
Bayesian Inference with Deep Weakly Nonlinear Networks—0
A Probabilistic Framework for Dynamic Object Recognition in 3D Environment With A Novel Continuous Ground Estimation Method—0
Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks—0
Bayesian inference via rejection filtering—0
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family—0
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology—0
Bayesian inference to improve quality of Retrieval Augmented Generation—0
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling—0
A theory of representation learning gives a deep generalisation of kernel methods—0
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Benchmark Results

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