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 1091–1100 of 2226 papers

TitleStatusHype
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
A Bayesian Inference Framework for Procedural Material Parameter Estimation—0
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning—0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
Deep Maxout Network Gaussian Process—0
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Benchmark Results

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