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Bayesian Inference

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

Papers

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Showing 1251–1275 of 2226 papers

TitleStatusHype
Structured Dropout Variational Inference for Bayesian Neural Networks—0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Neural Posterior Regularization for Likelihood-Free InferenceCode0
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problemsCode0
Bayesian Neural Networks for Virtual Flow Metering: An Empirical StudyCode0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Modeling German Word Order Acquisition via Bayesian Inference—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
Human Inference in Changing Environments With Temporal Structure—0
Bayesian Inference ForgettingCode0
Probabilistic Inference for Learning from Untrusted Sources—0
On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inferenceCode0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming—0
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs—0
Learning optimal Bayesian prior probabilities from data—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics—0
Bayesian Context Aggregation for Neural Processes—0
Variational Multi-Task Learning—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
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Benchmark Results

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