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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 1526–1550 of 2226 papers

TitleStatusHype
How To Make Your Cell Tracker Say "I dunno!"—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Human collective intelligence as distributed Bayesian inference—0
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability—0
Human Inference in Changing Environments With Temporal Structure—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties—0
Hybrid Predictive Coding: Inferring, Fast and Slow—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
IBIA: Bayesian Inference via Incremental Build-Infer-Approximate operations on Clique Trees—0
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Implicit Causal Models for Genome-wide Association Studies—0
Implicit Full Waveform Inversion with Deep Neural Representation—0
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping—0
Improved algorithm for neuronal ensemble inference by Monte Carlo method—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Combinatory Categorial Grammar Induction with Boundary Words and Bayesian Inference—0
Improved Neuronal Ensemble Inference with Generative Model and MCMC—0
Improvement and generalization of ABCD method with Bayesian inference—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Improving Generalization with Flat Hilbert Bayesian Inference—0
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Benchmark Results

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