Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Emanuele Pesce"'
Autor:
Giovanni Montana, Emanuele Pesce
Publikováno v:
Machine Learning. 109:1727-1747
Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc3a38d0bf499d000d204078b2d6d151
https://doi.org/10.1007/s10994-019-05864-5
https://doi.org/10.1007/s10994-019-05864-5
Autor:
Samuel Joseph Withey, Giovanni Montana, Robert Bakewell, Vicky Goh, Petros-Pavlos Ypsilantis, Emanuele Pesce
Publikováno v:
Pesce, E, Joseph Withey, S, Ypsilantis, P P, Bakewell, R, Goh, V & Montana, G 2019, ' Learning to detect chest radiographs containing pulmonary lesions using visual attention networks ', Medical Image Analysis, vol. 53, pp. 26-38 . https://doi.org/10.1016/j.media.2018.12.007
Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing av
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6708c548c4a3bbeff5bb5fe8b568c1a
https://doi.org/10.1016/j.media.2018.12.007
https://doi.org/10.1016/j.media.2018.12.007
Autor:
Emanuele Pesce, Giovanni Montana
In artificial multi-agent systems, the ability to learn collaborative policies is predicated upon the agents’ communication skills: they must be able to encode the information received from the environment and learn how to share it with other agent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c3faa24b08049c623b539c4b3119620
http://arxiv.org/abs/2002.05233
http://arxiv.org/abs/2002.05233
Autor:
Fabrizio Esposito, Emanuele Pesce, Gioacchino Tedeschi, Roberto Tagliaferri, Paola Galdi, Francesca Trojsi, Angela Serra, Michele Fratello
Publikováno v:
International journal of neural systems. 29(7)
Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dae0aa15974a89e70f33a3cd10273f0a
https://pubmed.ncbi.nlm.nih.gov/30929575
https://pubmed.ncbi.nlm.nih.gov/30929575
Publikováno v:
PRNI
Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field are towards representing brain con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::adbb4f02291a6f7b63d8f119cf7a4da5
http://arxiv.org/abs/1604.08912
http://arxiv.org/abs/1604.08912
Publikováno v:
SMC
With the advent of Social Web, the user has become an active consumer which shares information and participates in social networks, online communities, blogs, wikis, feeds and chats. The volunteer person-power is a valuable resource which creates inn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4dee4862141c0b644815bc2ed03a64c0
http://hdl.handle.net/11386/4679473
http://hdl.handle.net/11386/4679473
Publikováno v:
Lecture Notes in Information Systems and Organisation ISBN: 9783319237831
Understanding user intent during a web navigation session is a challenging topic. Existing approaches base such activity on many different features, including HCI features, which are also used by classifiers to determine the type of a web query. In t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7426d841b26cdfd3126bde948f2f96e
http://hdl.handle.net/11386/4658483
http://hdl.handle.net/11386/4658483