The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of the 3D scenes. However, it is solely concentrated on the appearance and geometry modeling, while lacking in fine-grained object-level scene understanding. To address this issue, we propose Gaussian Grouping, ...
Vecchia's approximate likelihood for Gaussian process parameters depends on how the observations are ordered, which has been cited as a deficiency. This article takes the alternative standpoint that the ordering can be tuned to sharpen the approximations. Indeed, the first part of the article ...
Permutation methods for sharpening Gaussian process approximations.Guinness, J. (2018) Permutation and Grouping Methods for Sharpening Gaussian Process Approx- imations. Technometrics.Guinness, J. (2016). Permutation methods for sharpening Gaussian process approximations. arXiv preprint arXiv:1609.05372 ....
Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise 来自 Semantic Scholar 喜欢 0 阅读量: 75 作者:I Pan,A Korre,S Das,S Durucan ...
Incorporating grouping information priors into the learning of Gaussian graphical models in a Bayesian framework is an innovative and feasible approach. We first introduce a Normal-Exponential-Gamma (NEG) structural mixture prior for the elements in the precision matrix. By embedding a structural ...
We present a kernel-independent method that applies hierarchical matrices to the problem of maximum likelihood estimation for Gaussian processes. The proposed approximation provides natural and scalable stochastic estimators for its gradient and Hessian, as well as the expected Fisher information matrix, ...