Zobrazeno 1 - 10
of 13 947
pro vyhledávání: '"Optimal Scaling"'
Autor:
Ibrahim, Maniru
The growing prevalence of high-dimensional data has fostered the development of multidimensional projection (MP) techniques, such as t-SNE, UMAP, and LAMP, for data visualization and exploration. However, conventional MP methods typically employ gene
Externí odkaz:
http://arxiv.org/abs/2407.16328
Kaplan et al. and Hoffmann et al. developed influential scaling laws for the optimal model size as a function of the compute budget, but these laws yield substantially different predictions. We explain the discrepancy by reproducing the Kaplan scalin
Externí odkaz:
http://arxiv.org/abs/2406.19146
Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property pred
Externí odkaz:
http://arxiv.org/abs/2402.06522
Autor:
Grasselli, Viviana
We consider the Schr{\"o}dinger operator --$\Delta$ + V on the Euclidean space with potential in the Lorentz space L^{n/2,1} and we find necessary and sufficient conditions for zero to be a resonance or an eigenvalue. We consider functions with gradi
Externí odkaz:
http://arxiv.org/abs/2403.13397
In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcom
Externí odkaz:
http://arxiv.org/abs/2309.00419
We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and non-differentiable targets. These methods have been shown to be stable
Externí odkaz:
http://arxiv.org/abs/2301.02446
Optimal scaling has been well studied for Metropolis-Hastings (M-H) algorithms in continuous spaces, but a similar understanding has been lacking in discrete spaces. Recently, a family of locally balanced proposals (LBP) for discrete spaces has been
Externí odkaz:
http://arxiv.org/abs/2209.08183
Autor:
Ning, Ning
Markov chain Monte Carlo (MCMC) algorithms have played a significant role in statistics, physics, machine learning and others, and they are the only known general and efficient approach for some high-dimensional problems. The random walk Metropolis (
Externí odkaz:
http://arxiv.org/abs/2210.17042
Autor:
Pillai, Natesh S.
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm that incorporates the gradient of the logarithm of the target density in its proposal distribution. In an earlier joint work \cite{pill:stu:12}, the author had extended the sem
Externí odkaz:
http://arxiv.org/abs/2204.10793
Autor:
Hoshiyar, Aisouda1 (AUTHOR) aisouda.hoshiyar@hsu-hh.de, Kiers, Henk A. L.2 (AUTHOR), Gertheiss, Jan1,3 (AUTHOR)
Publikováno v:
British Journal of Mathematical & Statistical Psychology. May2023, Vol. 76 Issue 2, p353-371. 19p.
