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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 1601–1650 of 2226 papers

TitleStatusHype
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family ApproximationsCode0
Hierarchical Bayesian myocardial perfusion quantification—0
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial SettingsCode0
Sparse Bayesian Learning Approach for Discrete Signal Reconstruction—0
Generalizing Eye Tracking With Bayesian Adversarial Learning—0
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
Bayesian Inference Semantics: A Modelling System and A Test SuiteCode0
Greedy inference with structure-exploiting lazy mapsCode0
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution TasksCode0
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators—0
Switching Linear Dynamics for Variational Bayes Filtering—0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over SimplexCode0
Data Augementation with Polya Inverse Gamma—0
Fast and Robust Rank Aggregation against Model MisspecificationCode0
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical SystemsCode0
Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set RecognitionCode0
Walsh-Hadamard Variational Inference for Bayesian Deep Learning—0
Variational Bayes: A report on approaches and applications—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian InferenceCode0
Decentralized Bayesian Learning over Graphs—0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data AugmentationCode0
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting—0
Accelerating Langevin Sampling with Birth-death—0
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-RiskCode0
Exploring helical dynamos with machine learningCode0
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations—0
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference—0
Variational approximations using Fisher divergence—0
Spectral Reconstruction with Deep Neural Networks—0
Stein Point Markov Chain Monte CarloCode0
A Latent Variational Framework for Stochastic Optimization—0
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluationsCode0
Variational Domain Adaptation—0
Neuromorphic Acceleration for Approximate Bayesian Inference on Neural Networks via Permanent Dropout—0
A Bayesian Monte Carlo approach for predicting the spread of infectious diseasesCode0
A Bayesian Perspective on the Deep Image PriorCode0
Reversible Jump Probabilistic ProgrammingCode0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Compressed sensing reconstruction using Expectation Propagation—0
A Generalization Bound for Online Variational Inference—0
Bayesian Neural Networks at Finite TemperatureCode0
The Kikuchi Hierarchy and Tensor PCA—0
Generalized Variational Inference: Three arguments for deriving new PosteriorsCode0
Robust Optimisation Monte CarloCode0
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints—0
Pairwise Comparisons with Flexible Time-DynamicsCode0
Combining Model and Parameter Uncertainty in Bayesian Neural NetworksCode0
Weighted Mean Curvature—0
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Benchmark Results

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