Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Trani, S."'
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task.
Externí odkaz:
http://arxiv.org/abs/2202.10728
Publikováno v:
Clinica Terapeutica; 2024 Supplement, Vol. 175, p205-208, 4p
Publikováno v:
Clinica Terapeutica; 2024 Supplement, Vol. 175, p44-46, 3p
Publikováno v:
IIR 2022-12th Italian Information Retrieval Workshop 2022, Tirrenia, Pisa, Italy, 19-22/06/2022
In this talk, we present the main results of a paper accepted at ECIR 2022 [1]. We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine learned ensembles of decision trees. Since the memory footprint of ranking
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______9984::a4a5864fe63fdb12cee8b274fd07df95
https://openportal.isti.cnr.it/doc?id=people______::53eee5fdce13ed0afa04f0192806cb14
https://openportal.isti.cnr.it/doc?id=people______::53eee5fdce13ed0afa04f0192806cb14
Publikováno v:
IEEE transactions on knowledge and data engineering (Online) 35 (2022): 4695–4712. doi:10.1109/TKDE.2022.3152585
IEEE transactions on knowledge and data engineering (Online) (2022): 103330. doi:10.1109/TKDE.2022.3152585
info:cnr-pdr/source/autori:Nardini F.M.; Rulli C.; Trani S.; Venturini R./titolo:Distilled neural networks for efficient learning to rank/doi:10.1109%2FTKDE.2022.3152585/rivista:IEEE transactions on knowledge and data engineering (Online)/anno:2022/pagina_da:/pagina_a:103330/intervallo_pagine:103330/volume
IEEE transactions on knowledge and data engineering (Online) (2022): 103330. doi:10.1109/TKDE.2022.3152585
info:cnr-pdr/source/autori:Nardini F.M.; Rulli C.; Trani S.; Venturini R./titolo:Distilled neural networks for efficient learning to rank/doi:10.1109%2FTKDE.2022.3152585/rivista:IEEE transactions on knowledge and data engineering (Online)/anno:2022/pagina_da:/pagina_a:103330/intervallo_pagine:103330/volume
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f7def747f41342da500302c3b37ebbc
Publikováno v:
SEBD 2022-30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the signi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______9984::09d32b97b94f29f6dde4988552e1bc19
https://openportal.isti.cnr.it/doc?id=people______::14bcea321d0d658a2513b20c867ff876
https://openportal.isti.cnr.it/doc?id=people______::14bcea321d0d658a2513b20c867ff876
Akademický článek
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Akademický článek
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Publikováno v:
Computational intelligence 34 (2018): 2–29. doi:10.1111/coin.12147
info:cnr-pdr/source/autori:Trani S.; Lucchese C.; Perego R.; Losada D.E.; Ceccarelli D.; Orlando S./titolo:SEL: a unified algorithm for salient entity linking/doi:10.1111%2Fcoin.12147/rivista:Computational intelligence/anno:2018/pagina_da:2/pagina_a:29/intervallo_pagine:2–29/volume:34
info:cnr-pdr/source/autori:Trani S.; Lucchese C.; Perego R.; Losada D.E.; Ceccarelli D.; Orlando S./titolo:SEL: a unified algorithm for salient entity linking/doi:10.1111%2Fcoin.12147/rivista:Computational intelligence/anno:2018/pagina_da:2/pagina_a:29/intervallo_pagine:2–29/volume:34
The entity linking task consists in automatically identifying and linking the entities mentioned in a text to their uniform resource identifiers in a given knowledge base. This task is very challenging due to its natural language ambiguity. However,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1823a49c566a51c59928879ff5513bac