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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 1051–1075 of 2226 papers

TitleStatusHype
A visual exploration of Gaussian Processes and Infinite Neural Networks—0
Bayesian sense of time in biological and artificial brains—0
Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture ModelsCode0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
Loss-calibrated expectation propagation for approximate Bayesian decision-making—0
SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery—0
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband ItemsCode0
Classifying Turbulent Environments via Machine Learning—0
Bayesian Invariant Risk Minimization—0
Inferring perceptual decision making parameters from behavior in production and reproduction tasks—0
Interpreting Dynamical Systems as Bayesian Reasoners—0
Bayesian Approaches to Shrinkage and Sparse EstimationCode0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
Data fission: splitting a single data point—0
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Predictive Coding Theories of Cortical Function—0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural NetworksCode0
AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers—0
Unified field theoretical approach to deep and recurrent neuronal networks—0
On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks—0
A Bayesian take on option pricing with Gaussian processes—0
Active Inference in Robotics and Artificial Agents: Survey and Challenges—0
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems—0
A sampling-based circuit for optimal decision making—0
Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation—0
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Benchmark Results

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