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 1901–1925 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
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Benchmark Results

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