Zobrazeno 1 - 10
of 3 717
pro vyhledávání: '"A. Bellavia"'
Autor:
Bellavia, Stefania, Malaspina, Greta
Consensus based optimization is a derivative-free particles-based method for the solution of global optimization problems. Several versions of the method have been proposed in the literature, and different convergence results have been proved. Howeve
Externí odkaz:
http://arxiv.org/abs/2408.10078
This work elaborates on the TRust-region-ish (TRish) algorithm, a stochastic optimization method for finite-sum minimization problems proposed by Curtis et al. in [Curtis2019, Curtis2022]. A theoretical analysis that complements the results in the li
Externí odkaz:
http://arxiv.org/abs/2404.13382
This paper introduces novel alternate training procedures for hard-parameter sharing Multi-Task Neural Networks (MTNNs). Traditional MTNN training faces challenges in managing conflicting loss gradients, often yielding sub-optimal performance. The pr
Externí odkaz:
http://arxiv.org/abs/2312.16340
An algorithm for unconstrained non-convex optimization is described, which does not evaluate the objective function and in which minimization is carried out, at each iteration, within a randomly selected subspace. It is shown that this random approxi
Externí odkaz:
http://arxiv.org/abs/2310.16580
Inexact Newton methods with matrix approximation by sampling for nonlinear least-squares and systems
We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and inexact Newton methods for nonlinear systems of equations. Random models are formed using suitable sampling strategies for the matrices involved i
Externí odkaz:
http://arxiv.org/abs/2310.05501
Adaptive cubic regularization methods for solving nonconvex problems need the efficient computation of the trial step, involving the minimization of a cubic model. We propose a new approach in which this model is minimized in a low dimensional subspa
Externí odkaz:
http://arxiv.org/abs/2306.14290
The spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stochastic optimization framework, especially in the case of mini-batch su
Externí odkaz:
http://arxiv.org/abs/2306.07379
Autor:
Bellavia, Fabio
Publikováno v:
IEEE Transactions on Image Processing, vol. 33, pp. 696-708, January 2024
This paper presents Slime, a novel non-deep image matching framework which models the scene as rough local overlapping planes. This intermediate representation sits in-between the local affine approximation of the keypoint patches and the global matc
Externí odkaz:
http://arxiv.org/abs/2305.08946
Autor:
Alessandro Mattina, Giuseppe Maria Raffa, Maria Ausilia Giusti, Elena Conoscenti, Marco Morsolini, Alessandra Mularoni, Maria Luisa Fazzina, Daniele Di Carlo, Manlio Cipriani, Francesco Musumeci, Antonio Arcadipane, Michele Pilato, Pier Giulio Conaldi, Diego Bellavia
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Detection of high glycated hemoglobin (A1c) is associated with worse postoperative outcomes, including predisposition to develop systemic and local infectious events. Diabetes and infectious Outcomes in Cardiac Surgery (DOCS) study is a retr
Externí odkaz:
https://doaj.org/article/47c34b7fd53e44259ccefe87bcc57dda
Autor:
Inge Varik, Runyu Zou, Andrea Bellavia, Kristine Rosenberg, Ylva Sjunnesson, Ida Hallberg, Jan Holte, Virissa Lenters, Majorie Van Duursen, Mikael Pedersen, Terje Svingen, Roel Vermeulen, Andres Salumets, Pauliina Damdimopoulou, Agne Velthut-Meikas
Publikováno v:
Environment International, Vol 191, Iss , Pp 108960- (2024)
The plasticizer di(2-ethylhexyl) phthalate (DEHP) is known to have endocrine-disrupting properties mediated by its many metabolites that form upon exposure in biological systems. In a previous study, we reported an inverse association between DEHP me
Externí odkaz:
https://doaj.org/article/a1fd408ea9254aa1a5ffd001ffe5d28b