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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 2151–2175 of 2226 papers

TitleStatusHype
Variational Autoencoders for Collaborative FilteringCode0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
Bayesian reconstruction of memories stored in neural networks from their connectivityCode0
Inferring Fitness in Finite Populations with Moran-like dynamicsCode0
Bayesian inference for PCA and MUSIC algorithms with unknown number of sourcesCode0
Particle Mean Field Variational BayesCode0
Teaching deep neural networks to localize single molecules for super-resolution microscopyCode0
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural NetworksCode0
Path-Guided Particle-based SamplingCode0
Approximate Bayesian Neural Doppler ImagingCode0
Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based ModelsCode0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noiseCode0
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon SynapseCode0
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty RegularizationCode0
Sensitivity-Aware Amortized Bayesian InferenceCode0
Amortized Variational Inference: When and Why?Code0
Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modelingCode0
Variational Sequential Monte CarloCode0
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object MeasurementsCode0
Extended molt phenology models improve inferences about molt duration and timingCode0
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive ModelsCode0
Bayesian Inference ForgettingCode0
Unbiased Bayesian Inference for Population Markov Jump Processes via Random TruncationsCode0
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial SettingsCode0
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Benchmark Results

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