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pro vyhledávání: '"DELLA VECCHIA A"'
We study algebraic shifting of uniform hypergraphs and finite simplicial complexes in the exterior algebra with respect to matrices which are not necessarily generic. Several questions raised by Kalai (2002) are addressed. For instance, it turns out
Externí odkaz:
http://arxiv.org/abs/2410.24044
We describe a generic JSON based file format which is suitable for computations in computer algebra. This is implemented in the computer algebra system OSCAR, but we also indicate how it can be used in a different context.
Externí odkaz:
http://arxiv.org/abs/2309.00465
Autor:
Della Vecchia, Andrea, Neocosmos, Kibidi, Larremore, Daniel B., Moore, Cristopher, De Bacco, Caterina
Publikováno v:
Phys. Rev. E 110, 034310, 2024
We present a physics-inspired method for inferring dynamic rankings in directed temporal networks - networks in which each directed and timestamped edge reflects the outcome and timing of a pairwise interaction. The inferred ranking of each node is r
Externí odkaz:
http://arxiv.org/abs/2307.13544
Autor:
Mathieu, Timothée, Della Vecchia, Riccardo, Shilova, Alena, Centa, Matheus Medeiros, Kohler, Hector, Maillard, Odalric-Ambrym, Preux, Philippe
Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To solve this reproducibility crisis, we propose a theoretically sound methodology to compare th
Externí odkaz:
http://arxiv.org/abs/2306.10882
Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Mac
Externí odkaz:
http://arxiv.org/abs/2304.07983
Autor:
Leonardo Borgato Della Vecchia, Caio Delano Campos Oliveira Assis, Fernando de Oliveira Salatiel, Maria Thereza Santos Cirino, Maria Eduarda Vogel Scarpante, Vanessa Monteiro Oliveira, Letícia Pedroso Meneghin, Maria Júlia Gonçalves Silva, Victória Ferini dos Santos, Natália Pavoni Catardo, Isabela Pulini Nemesio, Lívia Loamí Ruyz Jorge de Paula, Carolina Borges Garcia Sasdelli, Ana Beatriz Santos Bacchiega
Publikováno v:
Advances in Rheumatology, Vol 64, Iss 1, Pp 1-6 (2024)
Abstract Background In general, patients are referred for rheumatological evaluation due to isolated laboratory abnormalities, especially antinuclear antibody (ANA) positivity, with the risk of more severe patients remaining on the waiting list for l
Externí odkaz:
https://doaj.org/article/ebc782b3f1214bc08e45b16d1b1ff603
Endogeneity, i.e. the dependence of noise and covariates, is a common phenomenon in real data due to omitted variables, strategic behaviours, measurement errors etc. In contrast, the existing analyses of stochastic online linear regression with unbou
Externí odkaz:
http://arxiv.org/abs/2302.09357
We study a natural extension of classical empirical risk minimization, where the hypothesis space is a random subspace of a given space. In particular, we consider possibly data dependent subspaces spanned by a random subset of the data, recovering a
Externí odkaz:
http://arxiv.org/abs/2212.01866
Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL uses neural networks as function approximators. These neural models are
Externí odkaz:
http://arxiv.org/abs/2210.08503
Autor:
Giovanna Della Vecchia
Publikováno v:
Science & Philosophy, Vol 12, Iss 1 (2024)
Il presente lavoro intende ricostruire il percorso storico-epistemologico che ha condotto, nell’antichità, alla scoperta dell’esistenza delle grandezze incommensurabili.Non si sa con esattezza quando e come sia stata fatta tale importante scoper
Externí odkaz:
https://doaj.org/article/ea801f9313d94bd5bc49c57074492401