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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 1701–1750 of 2226 papers

TitleStatusHype
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments—0
Bandit Learning with Implicit FeedbackCode0
Bayesian Inference of Temporal Task Specifications from Demonstrations—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with -Divergences—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Stochastic Gradient MCMC with Repulsive ForcesCode0
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting—0
Uncertainty propagation in neural networks for sparse coding—0
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference—0
Amortized Bayesian inference for clustering modelsCode0
Surrogate-assisted parallel tempering for Bayesian neural learningCode0
Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching HeteroskedasticityCode0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis—0
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsCode0
BAR: Bayesian Activity Recognition using variational inference—0
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning—0
Learning and Inference in Hilbert Space with Quantum Graphical Models—0
Towards Principled Uncertainty Estimation for Deep Neural Networks—0
Mean-field theory of graph neural networks in graph partitioning—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
Stochastic Gradient MCMC for State Space ModelsCode0
Good Initializations of Variational Bayes for Deep Models—0
EMHMM Simulation Study—0
Metropolis-Hastings view on variational inference and adversarial training—0
The Deep Weight PriorCode0
Bayesian Inference of Self-intention Attributed by Observer—0
Uncertainty in Neural Networks: Approximately Bayesian EnsemblingCode0
Dropout as a Structured Shrinkage PriorCode0
An easy-to-use empirical likelihood ABC method—0
Sketching for Latent Dirichlet-Categorical Models—0
Uncertainty-aware generative models for inferring document class prevalenceCode0
Bayesian Prediction of Future Street Scenes using Synthetic LikelihoodsCode0
Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers—0
Adaptive Gaussian process surrogates for Bayesian inference—0
Bayesian inference for PCA and MUSIC algorithms with unknown number of sourcesCode0
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach—0
Flexible Mixture Modeling on Constrained Spaces—0
Simulator Calibration under Covariate Shift with Kernels—0
Stochasticity from function -- why the Bayesian brain may need no noise—0
Predictive Collective Variable Discovery with Deep Bayesian ModelsCode0
Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data—0
Robustness Guarantees for Bayesian Inference with Gaussian ProcessesCode0
A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM—0
Seeing Tree Structure from Vibration—0
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting—0
Differentially Private Bayesian Inference for Exponential FamiliesCode0
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion—0
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning—0
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Benchmark Results

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