Bayesian neural networks for detecting epistasis in genetic association studies
PulseAugur coverage of Bayesian neural networks for detecting epistasis in genetic association studies — every cluster mentioning Bayesian neural networks for detecting epistasis in genetic association studies across labs, papers, and developer communities, ranked by signal.
No coverage in the last 90 days.
2 day(s) with sentiment data
-
New theory explores Bayesian Neural Networks with dependent weights
Researchers have developed a new theoretical framework for understanding Bayesian Neural Networks (BNNs) with dependent weights. This work extends previous findings by analyzing the posterior distribution of BNN outputs…
-
New SSLA method improves Bayesian model uncertainty quantification
Researchers have developed a new method called Self-Supervised Laplace Approximation (SSLA) to directly approximate the posterior predictive distribution in Bayesian models. This approach draws inspiration from self-tra…
-
New AI research explores advanced methods for uncertainty estimation and Bayesian inference
Researchers have developed a new variational Bayesian framework that directly targets the posterior-predictive distribution, jointly learning approximations for both the posterior and predictive distributions. This appr…
-
Singular Bayesian Neural Networks
Researchers have introduced Singular Bayesian Neural Networks, a novel approach that significantly reduces the parameter count required for Bayesian neural networks. By parameterizing weights using a low-rank decomposit…
-
Bayesian Neural Networks gain lightweight heteroscedastic uncertainty inference
Researchers have developed a new framework for Bayesian Neural Networks (BNNs) that efficiently incorporates heteroscedastic uncertainties. This approach embeds both aleatoric and epistemic variances into the BNN parame…
-
Bayesian Neural Kalman Filter enhances UAV state estimation in noisy environments
Researchers have developed a new Bayesian Neural Kalman Filter (BNKF) to improve state estimation for unmanned aerial vehicles (UAVs) in challenging environments. This hybrid framework combines Bayesian Neural Networks …
-
Bayesian Tensor Network Kernel Machines use Laplace approximation for uncertainty estimation
Researchers have developed a new Bayesian Tensor Network Kernel Machine (LA-TNKM) that utilizes a linearized Laplace approximation for inference. This method addresses the challenge of providing uncertainty estimates in…