Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Elvin Isufi"'
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 186-194 (2024)
The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresp
Externí odkaz:
https://doaj.org/article/cc93507855064784bf60db40d5e30fa0
Autor:
Bulat Kerimov, Roberto Bentivoglio, Alexander Garzón, Elvin Isufi, Franz Tscheikner-Gratl, David Bernhard Steffelbauer, Riccardo Taormina
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 6, Pp 2223-2234 (2023)
Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applicatio
Externí odkaz:
https://doaj.org/article/e6dc167defd54b20ba72810db82e213f
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 4, Pp 61-70 (2023)
An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and t
Externí odkaz:
https://doaj.org/article/aee2ea83515c4f00b74957c53f124538
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 3, Pp 212-228 (2022)
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:
https://doaj.org/article/466a04914bd44caf899287e0ca2a3668
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 2, Pp 85-98 (2021)
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, we propose a node-adaptive regularization for graph signal reconstructi
Externí odkaz:
https://doaj.org/article/f475a87c441f4f4f8d457cd55c0d0717
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 44:7457-7473
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97d4ef38b5c3e8e30a00c8c3b3c873ee
https://doi.org/10.1109/tpami.2021.3111054
https://doi.org/10.1109/tpami.2021.3111054
Autor:
Bishwadeep Das, Elvin Isufi
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::968f8e6e897ffc846abd355d5c1af49e
https://doi.org/10.1109/icassp49357.2023.10096508
https://doi.org/10.1109/icassp49357.2023.10096508
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96c07c51e7bb1b0a73d99eeb91aef47e
https://doi.org/10.1109/icassp49357.2023.10096095
https://doi.org/10.1109/icassp49357.2023.10096095
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5e9f2c5061b4684cd1d78f48766c58c
https://doi.org/10.1109/icassp49357.2023.10096364
https://doi.org/10.1109/icassp49357.2023.10096364
The high computational cost of detailed numerical models for flood simulation hinders their use in real-time and limits uncertainty quantification. Deep-learning surrogates have thus emerged as an alternative to speed up simulations. However, most su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18331492a0c3155c61d0a4a4990412d9
https://doi.org/10.5194/egusphere-egu23-12952
https://doi.org/10.5194/egusphere-egu23-12952
