Laplace Approximation
PulseAugur coverage of Laplace Approximation — every cluster mentioning Laplace Approximation across labs, papers, and developer communities, ranked by signal.
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New methods advance uncertainty quantification in machine learning · 5 sources tracked
Researchers have introduced new methods for evaluating uncertainty quantification (UQ) in machine learning models. One approach, termed "decision-alignment," aims to ensure that UQ metrics meaningfully correlate with do…
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Deep learning framework accelerates CO2 retrieval from satellite data
Researchers have developed a novel deep learning framework to more efficiently and accurately retrieve atmospheric carbon dioxide (CO2) data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. This new method u…
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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…
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New methods improve Laplace approximation for neural network uncertainty
Researchers have developed new methods for approximating the Laplace approximation in deep neural networks, addressing the computational challenges of inverting large Hessian matrices. The proposed Gradient-Laplace and …
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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…