Zobrazeno 1 - 10
of 548
pro vyhledávání: '"P. Ialongo"'
Autor:
Besner, Alexandre, Massé, Alexandre Blondin, Bani, Abderrahman, Morabit, Mouad, Berthaut, François, Charest, Luc, Ialongo, David, Mbeutcha, Yves, Couture-Gagnon, Simon, Fournier, Julien
Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits, and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by large hydroelectric dams located far
Externí odkaz:
http://arxiv.org/abs/2405.20199
Publikováno v:
Journal of the Belgian Society of Radiology, Vol 98, Iss 1, Pp 3-19 (2015)
Connective tissue diseases (CTDs) are a heterogeneous group of inflammatory diseases derived from an immunologic deregulation affecting various organs. A thoracic involvement(pulmonary, pleural or mediastinal)can be frequently found; its frequency an
Externí odkaz:
https://doaj.org/article/0bb6084c77d64960a05cb50a12cd142b
Autor:
Ialongo, Leonardo Niccolò, de Valk, Camille, Marchese, Emiliano, Jansen, Fabian, Zmarrou, Hicham, Squartini, Tiziano, Garlaschelli, Diego
Publikováno v:
Sci. Rep. 12 (11847) (2022)
Recent crises have shown that the knowledge of the structure of input-output networks at the firm level is crucial when studying economic resilience from the microscopic point of view of firms that rewire their connections under supply and demand sho
Externí odkaz:
http://arxiv.org/abs/2111.15248
Autor:
Byravan, Arunkumar, Hasenclever, Leonard, Trochim, Piotr, Mirza, Mehdi, Ialongo, Alessandro Davide, Tassa, Yuval, Springenberg, Jost Tobias, Abdolmaleki, Abbas, Heess, Nicolas, Merel, Josh, Riedmiller, Martin
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid app
Externí odkaz:
http://arxiv.org/abs/2110.03363
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite
Externí odkaz:
https://doaj.org/article/64080b36ff2b46f8b99d882a41b5d2fd
Policy networks are a central feature of deep reinforcement learning (RL) algorithms for continuous control, enabling the estimation and sampling of high-value actions. From the variational inference perspective on RL, policy networks, when used with
Externí odkaz:
http://arxiv.org/abs/2010.10670
Autor:
Antonella Messore, Paolo Malune, Elisa Patacchini, Valentina Noemi Madia, Davide Ialongo, Merve Arpacioglu, Aurora Albano, Giuseppe Ruggieri, Francesco Saccoliti, Luigi Scipione, Enzo Tramontano, Serena Canton, Angela Corona, Sante Scognamiglio, Annalaura Paulis, Mustapha Suleiman, Helmi Mohammed Al-Maqtari, Fatma Mohamed A. Abid, Sarkar M. A. Kawsar, Murugesan Sankaranarayanan, Roberto Di Santo, Francesca Esposito, Roberta Costi
Publikováno v:
Pharmaceuticals, Vol 17, Iss 5, p 650 (2024)
It has been more than four years since the first report of SARS-CoV-2, and humankind has experienced a pandemic with an unprecedented impact. Moreover, the new variants have made the situation even worse. Among viral enzymes, the SARS-CoV-2 main prot
Externí odkaz:
https://doaj.org/article/33897d42e8a5438eb1decd73c9215d74
Publikováno v:
PMLR 97:2931-2940 (2019)
We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the
Externí odkaz:
http://arxiv.org/abs/1906.05828
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so far has been to use a factorised posterior distribution, decoupling the
Externí odkaz:
http://arxiv.org/abs/1812.06067
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling. Our approach performs variational inference over both the system state
Externí odkaz:
http://arxiv.org/abs/1812.03580