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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 1201–1250 of 2226 papers

TitleStatusHype
Metropolis-CVAE: Bootstrapping Labels for Bayesian Inference via Semi-Supervised Conditional Variational Autoencoders—0
Quantifying the mini-batching error in Bayesian inference for Adaptive Langevin dynamics—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
Improved Neuronal Ensemble Inference with Generative Model and MCMC—0
Nonlinear Hawkes Process with Gaussian Process Self Effects—0
Parametrization invariant interpretation of priors and posteriors—0
Bayesian reconstruction of memories stored in neural networks from their connectivityCode0
Attentional Prototype Inference for Few-Shot SegmentationCode0
Priors in Bayesian Deep Learning: A Review—0
High-Performance FPGA-based Accelerator for Bayesian Neural Networks—0
Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—0
Laplace Matching for fast Approximate Inference in Latent Gaussian ModelsCode0
MCMC-driven importance samplers—0
Bayesian Logistic Shape Model Inference: application to cochlea image segmentation—0
Consumer Demand Modeling During COVID-19 Pandemic—0
Regularizing Explanations in Bayesian Convolutional Neural Networks—0
Invariant polynomials and machine learningCode0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Bayesian Numerical Methods for Nonlinear Partial Differential Equations—0
Robust Generalised Bayesian Inference for Intractable LikelihoodsCode0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inferenceCode0
The computational asymptotics of Gaussian variational inference and the Laplace approximationCode0
Revisiting Bayesian Autoencoders with MCMCCode0
COVID-19 detection using chest X-rays: is lung segmentation important for generalization?Code0
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC—0
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
A Bayesian Approach to Identifying Representational Errors—0
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality—0
Adaptive Degradation Process with Deep Learning-Driven Trajectory—0
Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development—0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks—0
A hierarchical Bayesian model to find brain-behaviour associations in incomplete data setsCode0
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs—0
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie—0
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphsCode0
A Hamiltonian Monte Carlo Model for Imputation and Augmentation of Healthcare DataCode0
Parsimonious Inference—0
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders—0
Stein Variational Gradient Descent: many-particle and long-time asymptotics—0
Sparse online variational Bayesian regressionCode0
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference—0
Estimating the impact of non-pharmaceutical interventions and vaccination on the progress of the COVID-19 epidemic in Mexico: a mathematical approach—0
The Variational Bayesian Inference for Network Autoregression Models—0
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Benchmark Results

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