Zobrazeno 1 - 10
of 1 891
pro vyhledávání: '"Stochastic variational inference"'
Like many optimization algorithms, Stochastic Variational Inference (SVI) is sensitive to the choice of the learning rate. If the learning rate is too small, the optimization process may be slow, and the algorithm might get stuck in local optima. On
Externí odkaz:
http://arxiv.org/abs/2412.15745
In structured additive distributional regression, the conditional distribution of the response variables given the covariate information and the vector of model parameters is modelled using a P-parametric probability density function where each param
Externí odkaz:
http://arxiv.org/abs/2412.10038
The Multi-Output Gaussian Process is is a popular tool for modelling data from multiple sources. A typical choice to build a covariance function for a MOGP is the Linear Model of Coregionalization (LMC) which parametrically models the covariance betw
Externí odkaz:
http://arxiv.org/abs/2407.02476
In this paper, we propose a novel method of model-based time series clustering with mixtures of general state space models (MSSMs). Each component of MSSMs is associated with each cluster. An advantage of the proposed method is that it enables the us
Externí odkaz:
http://arxiv.org/abs/2407.00429
Autor:
Dayta, Dominic B.
We introduce YOASOVI, an algorithm for performing fast, self-correcting stochastic optimization for Variational Inference (VI) on large Bayesian heirarchical models. To accomplish this, we take advantage of available information on the objective func
Externí odkaz:
http://arxiv.org/abs/2406.02838
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Stochastic variational inference algorithms are derived for fitting various heteroskedastic time series models. We examine Gaussian, t, and skew-t response GARCH models and fit these using Gaussian variational approximating densities. We implement ef
Externí odkaz:
http://arxiv.org/abs/2308.14952
Autor:
Amin, Ahmad Ayaz
Stochastic variational inference and its derivatives in the form of variational autoencoders enjoy the ability to perform Bayesian inference on large datasets in an efficient manner. However, performing inference with a VAE requires a certain design
Externí odkaz:
http://arxiv.org/abs/2308.08053
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
International Journal of Transportation Science and Technology, Vol 15, Iss , Pp 181-197 (2024)
Due to the increasing demand for goods movement, externalities from freight mobility have attracted much concern among local citizens and policymakers. Freight truck-related crash is one of these externalities and impacts urban freight transportation
Externí odkaz:
https://doaj.org/article/e7450f145e20455e947f782f1ba224ff
