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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 1601–1625 of 2226 papers

TitleStatusHype
Kalman filters as the steady-state solution of gradient descent on variational free energy—0
Kernel Bayesian Inference with Posterior Regularization—0
Kernel Bayes' Rule—0
Kernel Stein Generative Modeling—0
Knowing when we do not know: Bayesian continual learning for sensing-based analysis tasks—0
Online Label Aggregation: A Variational Bayesian Approach—0
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process—0
Bayesian Consensus: Consensus Estimates from Miscalibrated Instruments under Heteroscedastic Noise—0
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Latent Variable Models for Bayesian Causal Discovery—0
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks—0
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference—0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Learning and Inference in Hilbert Space with Quantum Graphical Models—0
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications—0
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective—0
Learning Curves for Deep Neural Networks: A field theory perspective—0
Learning Deep Generative Models with Doubly Stochastic MCMC—0
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Learning Inference Models for Computer Vision—0
Learning Latent Structural Causal Models—0
Learning Manifold Implicitly via Explicit Heat-Kernel Learning—0
Learning non-concatenative morphology—0
Learning Not to Learn: Nature versus Nurture in Silico—0
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Benchmark Results

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