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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 1401–1425 of 2226 papers

TitleStatusHype
Fast Burst-Sparsity Learning Approach for Massive MIMO-OTFS Channel Estimation—0
Fast Convergence for Langevin Diffusion with Manifold Structure—0
Fast Convergence for Langevin with Matrix Manifold Structure—0
Fast Dual Variational Inference for Non-Conjugate LGMs—0
Sampling from Log-Concave Distributions over Polytopes via a Soft-Threshold Dikin Walk—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Fast Parallel SVM using Data Augmentation—0
Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes—0
Approximations in the homogeneous Ising model—0
Fast Variational Inference for Large-scale Internet Diagnosis—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—0
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing—0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble—0
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout—0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms—0
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering—0
Federated Variational Inference: Towards Improved Personalization and Generalization—0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
Few-Shot Bayesian Imitation Learning with Logical Program Policies—0
Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization—0
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection—0
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Benchmark Results

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