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 framework simplifies neural network mixed-effects model implementation
Researchers have developed a new framework for implementing neural network mixed-effects models (NMMs) using Template Model Builder (TMB). This approach leverages automatic differentiation and Laplace approximation, all…
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New Bayesian Explanation Method Enhances Power Quality Disturbance Classifier Reliability
This paper introduces a novel post-hoc Bayesian explanation method for deep learning classifiers used in power quality disturbance recognition. The method employs a Laplace approximation to efficiently derive an approxi…
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New Bayesian framework improves structural health monitoring by removing EOV
Researchers have developed a novel Bayesian framework for structural health monitoring (SHM) that simultaneously identifies and removes environmental and operational variability (EOV). This approach models the latent EO…
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New method Adaptive KappaSharp enhances Preferential Bayesian Optimization
Researchers have developed Adaptive KappaSharp, a novel method to improve the efficiency of Preferential Bayesian Optimization (PBO). PBO is used to optimize objectives based on pairwise user comparisons. The new techni…
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New research compares MCMC, LA, and VI complexity for generalized linear models
A new arXiv paper explores the computational complexity of Markov Chain Monte Carlo (MCMC) methods for generalized linear models, comparing them to Laplace approximation (LA) and variational inference (VI). The research…
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New methods improve uncertainty quantification in deep neural networks
A new paper introduces two novel methods, Gradient-Laplace and Greedy-Laplace, for approximating Hessian matrices in deep neural networks. These methods aim to improve uncertainty quantification by addressing the comput…
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New sparse Gaussian process framework tackles quantile regression challenges
Researchers have developed a novel sparse Gaussian process framework to address the computational challenges in Bayesian quantile regression. This new approach utilizes a reduced set of inducing variables and a Laplace …
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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…