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pro vyhledávání: '"Natali, Alberto"'
Autor:
Natali, Alberto, Leus, Geert
In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying filters. Specifically, we show how a node-wise convolution for signals
Externí odkaz:
http://arxiv.org/abs/2312.16922
Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a scalar one, existing approaches resort to the use of product graphs to
Externí odkaz:
http://arxiv.org/abs/2210.15258
Autor:
Natali, Alberto, Leus, Geert
Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of
Externí odkaz:
http://arxiv.org/abs/2210.11874
This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated
Externí odkaz:
http://arxiv.org/abs/2110.11017
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has
Externí odkaz:
http://arxiv.org/abs/2010.11634
Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weight
Externí odkaz:
http://arxiv.org/abs/2007.03266
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a v
Externí odkaz:
http://arxiv.org/abs/2004.08260
Akademický článek
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Autor:
Benedetti, Manuel, Vecchi, Valeria, Betterle, Nico, Natali, Alberto, Bassi, Roberto, Dall’Osto, Luca
Publikováno v:
In Journal of Biotechnology 20 April 2019 296:42-52
Autor:
Natali, Alberto, Leus, Geert
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of