SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1901–1950 of 2226 papers

TitleStatusHype
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series—0
Bidirectional Recurrent Neural Networks as Generative Models—0
BI-EqNO: Generalized Approximate Bayesian Inference with an Equivariant Neural Operator Framework—0
Big Learning with Bayesian Methods—0
Bilinear Subspace Variational Bayesian Inference for Joint Scattering Environment Sensing and Data Recovery in ISAC Systems—0
Biogeochemistry-Informed Neural Network (BINN) for Improving Accuracy of Model Prediction and Scientific Understanding of Soil Organic Carbon—0
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks—0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Black-box Bayesian inference for economic agent-based models—0
Blindness of score-based methods to isolated components and mixing proportions—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Body movement to sound interface with vector autoregressive hierarchical hidden Markov models—0
Bone fusion in normal and pathological development is constrained by the network architecture of the human skull—0
Bootstrapped synthetic likelihood—0
Bounded rationality in structured density estimation—0
Bounded rationality in structured density estimation—0
Bridging the reality gap in quantum devices with physics-aware machine learning—0
Bridging the Sim-to-Real Gap with Bayesian Inference—0
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning—0
Building fast Bayesian computing machines out of intentionally stochastic, digital parts—0
Building general Langevin models from discrete data sets—0
Burn-in, bias, and the rationality of anchoring—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
Calibration and Uncertainty Quantification of Bayesian Convolutional Neural Networks for Geophysical Applications—0
Calibration of Model Uncertainty for Dropout Variational Inference—0
Can Bayesian Neural Networks Make Confident Predictions?—0
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference—0
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control—0
Can Sequential Bayesian Inference Solve Continual Learning?—0
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning—0
Cascaded Calibration of Mechatronic Systems via Bayesian Inference—0
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models—0
Causal Inference through a Witness Protection Program—0
Predictive Coding beyond Correlations—0
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems—0
Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds—0
Characteristics of Monte Carlo Dropout in Wide Neural Networks—0
Classified as unknown: A novel Bayesian neural network—0
Clustered Mallows Model—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
Coherent Track-Before-Detect—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
Cold Posteriors through PAC-Bayes—0
Collapsed Variational Bayesian Inference for PCFGs—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
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Benchmark Results

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