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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 976–1000 of 2226 papers

TitleStatusHype
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
Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication—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
A time-varying finance-led model for U.S. business cycles—0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms—0
Fully Bayesian inference for neural models with negative-binomial spiking—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
Functional Distributional Semantics—0
Bayesian Learning for Neural Networks: an algorithmic survey—0
Bayesian Learning of Kernel Embeddings—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
Firefly Monte Carlo: Exact MCMC with Subsets of Data—0
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs—0
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation—0
A theory of data variability in Neural Network Bayesian inference—0
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Benchmark Results

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