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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 751–800 of 2226 papers

TitleStatusHype
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks—0
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain—0
Amortized Bayesian Inference of GISAXS Data with Normalizing FlowsCode0
Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks—0
Generalized second law of thermodynamics in the Glosten-Milgrom model—0
Accurate, reliable and interpretable solubility prediction of druglike molecules with attention pooling and Bayesian learning—0
Feature Selection via the Intervened Interpolative Decomposition and its Application in Diversifying Quantitative Strategies—0
Hamiltonian Adaptive Importance Sampling—0
Ensemble-based gradient inference for particle methods in optimization and samplingCode0
Batch Bayesian optimisation via density-ratio estimation with guaranteesCode0
Convolutional Bayesian Kernel Inference for 3D Semantic MappingCode1
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference—0
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal 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
Topology Change Aware Data-Driven Probabilistic Distribution State Estimation Based on Gaussian Process—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
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
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact caseCode1
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
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming—0
Graph-based sequential beamformingCode0
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation—0
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging—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
Bayesian Inference with Latent Hamiltonian Neural NetworksCode1
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy—0
Deep Maxout Network Gaussian Process—0
Optimal Rates for Regularized Conditional Mean Embedding Learning—0
Enhanced gradient-based MCMC in discrete spaces—0
Reliable amortized variational inference with physics-based latent distribution correctionCode1
Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference PerspectiveCode0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
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Benchmark Results

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