Výsledky vyhledávání - "Raffaele, Perego"

Akademický článek

Early Exit Strategies for Learning-to-Rank Cascades

Autor: Francesco Busolin, Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani

Publikováno v: IEEE Access, Vol 11, Pp 126691-126704 (2023)

The ranking pipelines of modern search platforms commonly exploit complex machine-learned models and have a significant impact on the query response time. In this paper, we discuss several techniques to speed up the document scoring process based on

Externí odkaz: https://doaj.org/article/e3b25de9f0424526ae61793a43ff02df

Zobrazit plný text záznamu

Akademický článek

RankEval: Evaluation and investigation of ranking models

Autor: Claudio Lucchese, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Salvatore Trani

Publikováno v: SoftwareX, Vol 12, Iss , Pp 100614- (2020)

RankEval is a Python open-source tool for the analysis and evaluation of ranking models based on ensembles of decision trees. Learning-to-Rank (LtR) approaches that generate tree-ensembles are considered the most effective solution for difficult rank

Externí odkaz: https://doaj.org/article/5a6ce575423a45dc80eb79de607e1d04

Zobrazit plný text záznamu

Akademický článek

Empowering knowledge generation through international data network: the IMeCCHI-DATANETWORK

Autor: Lucia Otero Varela, Marie-Annick Le Pogam, Amy Metcalfe, Pia Kjær Kristensen, Phil Hider, Alka Patel, Hongsoo Kim, Emanuele Carlini, Raffaele Perego, Rosa Gini

Publikováno v: International Journal of Population Data Science, Vol 5, Iss 1 (2020)

Introduction The International Methodology Consortium for Coded Health Information (IMeCCHI) is a collaboration of health services researchers who promote methodological advances in coded health information. The IMeCCHI-DATANETWORK initiative focuses

Externí odkaz: https://doaj.org/article/a12efdd6d4564b87bad3cd9996a040ae

Zobrazit plný text záznamu

Efficient traversal of decision tree ensembles with FPGAs

Autor: Veronica Gil-Costa, Fernando Loor, Salvatore Trani, Franco Maria Nardini, Romina Soledad Molina, Raffaele Perego

Publikováno v: Journal of parallel and distributed computing
155 (2021): 38–49. doi:10.1016/j.jpdc.2021.04.008
info:cnr-pdr/source/autori:Molina R.; Loor F.; Gil-Costa V.; Nardini F.M.; Perego R.; Trani S./titolo:Efficient traversal of decision tree ensembles with FPGAs/doi:10.1016%2Fj.jpdc.2021.04.008/rivista:Journal of parallel and distributed computing (Print)/anno:2021/pagina_da:38/pagina_a:49/intervallo_pagine:38–49/volume:155

System-on-Chip (SoC) based Field Programmable Gate Arrays (FPGAs) provide a hardware acceleration technology that can be rapidly deployed and tuned, thus providing a flexible solution adaptable to specific design requirements and to changing demands.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4474a5ce69ea7a2daf920550cb8c747a
https://doi.org/10.1016/j.jpdc.2021.04.008

Zobrazit plný text záznamu

Akademický článek

IMECCHI-DATANETWORK: empowering knowledge generation through international data network

Autor: Marie Annick Le Pogam, Amy Metcalf, Søren Paaske Johnsen, Phil Hider, Alka Patel, Hongsoo Kim, Emanuele Carlini, Raffaele Perego, Hude Quan, Rosa Gini

Publikováno v: International Journal of Population Data Science, Vol 1, Iss 1 (2017)

ABSTRACT Objectives The International Methodology Consortium for Coded Health Information (IMECCHI), an international collaboration of health services researchers, launched the IMECCHI-DATANETWORK initiative in October 2015. Its main objective is

Externí odkaz: https://doaj.org/article/14f74323e4924bf7a91a41aeca43680f

Zobrazit plný text záznamu

ILMART: Interpretable Ranking with Constrained LambdaMART

Autor: Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Alberto Veneri

Publikováno v: SIGIR '22-45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255–2259, Madrid, Spain, 11-15/07/2022

Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7090e7c764ef7b847f6291026a1278e6
https://doi.org/10.1145/3477495.3531840

Zobrazit plný text záznamu

Report on the 43rd european conference on information retrieval (ECIR 2021)

Autor: Fabrizio Sebastiani, Raffaele Perego

Publikováno v: ACM SIGIR Forum. 55:1-5

The 43rd European Conference on Information Retrieval (ECIR 2021), organized under the auspices of the Information Retrieval Specialist Group of the British Computer Society (BCS IRSG), took place between March 28 and April 1, 2021. As sadly customar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::35aef51d7106635621547b7fc07eb1fe
https://doi.org/10.1145/3476415.3476424

Zobrazit plný text záznamu

A Co-occurrence Based Approach for Mining Overlapped Co-clusters in Binary Data

Autor: Matheus Henrique Schaly, Rafael de Santiago, Vania Bogorny, Chiara Renso, Luis Otavio Alvares, Yuri Santa Rosa Nassar dos Santos, Raffaele Perego

Publikováno v: BRACIS 2021-10th Brazilian Conference on Intelligent Systems, pp. 375–389, Online Conference, 29/11/2021-3/12/2021
Intelligent Systems ISBN: 9783030917012
info:cnr-pdr/source/autori:Santa Rosa Nassar dos Santos Y.; de Santiago R.; Perego R.; Schaly M.H.; Alvares L.O.; Renso C.; Bogorny V./congresso_nome:BRACIS 2021-10th Brazilian Conference on Intelligent Systems/congresso_luogo:Online Conference/congresso_data:29%2F11%2F2021-3%2F12%2F2021/anno:2021/pagina_da:375/pagina_a:389/intervallo_pagine:375–389

Co-clustering is a specific type of clustering that addresses the problem of simultaneously clustering objects and attributes of a data matrix. Although general clustering techniques find non-overlapping co-clusters, finding possible overlaps between

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba5bd6529b44c17dea084462fbdb8995
https://zenodo.org/record/5970008

Zobrazit plný text záznamu

MAT-Index: An index for fast multiple aspect trajectory similarity measuring

Autor: Ana Paula Ramos de Souza, Chiara Renso, Raffaele Perego, Vania Bogorny

The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks like querying,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2659::be3d11a4f1647834ae110086649eef65
https://zenodo.org/record/5902276

Zobrazit plný text záznamu

A Dependency-Aware Utterances Permutation Strategy to Improve Conversational Evaluation

Autor: Nicola Ferro, NICOLA TONELLOTTO, Raffaele Perego, Marco FERRANTE, Guglielmo Faggioli

Publikováno v: ECIR 2022-44th European Conference on IR Research, pp. 184–198, Stavanger, Norway, 10-14/04/2022
Lecture Notes in Computer Science ISBN: 9783030997359

The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores infor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e54cc3ec169eabe85f9dac78fd6e018e
http://hdl.handle.net/11577/3443461

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání