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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 901–925 of 2226 papers

TitleStatusHype
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Implicit Full Waveform Inversion with Deep Neural Representation—0
Non-Gaussian Process Regression—0
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution—0
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference—0
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation—0
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Better Peer Grading through Bayesian InferenceCode0
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization—0
Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference—0
Conjugate Natural Selection—0
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation—0
Graph-based sequential beamformingCode0
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging—0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
Bayesian Floor Field: Transferring people flow predictions across environmentsCode0
Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers—0
Scale invariant process regression: Towards Bayesian ML with minimal assumptions—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
Deep Maxout Network Gaussian Process—0
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Benchmark Results

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