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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 1101–1150 of 2226 papers

TitleStatusHype
The Case for Bayesian Deep Learning—0
The challenge of uncertainty quantification of large language models in medicine—0
The Compositional Structure of Bayesian Inference—0
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems—0
The equivalence between Stein variational gradient descent and black-box variational inference—0
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
The high-conductance state enables neural sampling in networks of LIF neurons—0
The Inverse of Exact Renormalization Group Flows as Statistical Inference—0
The Kikuchi Hierarchy and Tensor PCA—0
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
The linear conditional expectation in Hilbert space—0
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection—0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
The Population Posterior and Bayesian Modeling on Streams—0
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain—0
The Recycling Gibbs Sampler for Efficient Learning—0
Thermodynamic Bayesian Inference—0
The Stochastic complexity of spin models: Are pairwise models really simple?—0
The Variational Bayesian Inference for Network Autoregression Models—0
The Variational InfoMax Learning Objective—0
The variational Laplace approach to approximate Bayesian inference—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
Thompson Sampling on Symmetric α-Stable Bandits—0
Hardware implementation of timely reliable Bayesian decision-making using memristors—0
Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference—0
Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function—0
Toward Biochemical Probabilistic Computation—0
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization—0
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization—0
Towards an Author-Topic-Term-Model Visualization of 100 Years of German Sociological Society Proceedings—0
Towards a Unified Framework for Sequential Decision Making—0
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification—0
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry—0
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies—0
Towards Machine Wald—0
Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior—0
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task—0
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
Towards Scalable Bayesian Learning of Causal DAGs—0
Towards Unifying Perceptual Reasoning and Logical Reasoning—0
Tracking disease outbreaks from sparse data with Bayesian inference—0
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory—0
Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework—0
Trajectory Modeling via Random Utility Inverse Reinforcement Learning—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Transport Gaussian Processes for Regression—0
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Benchmark Results

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