Výsledky vyhledávání - "Marcelo Grave"

How can AI leverage alternative criteria and suggest a better way to measure credit worthiness and economic growth?

Autor: Heloisa Candello, Marcelo Grave, Emilio Brazil, Marina Ito, Adinan Alves de Brito Filho, Rogerio de Paula

Publikováno v: 4th Conference on Conversational User Interfaces.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a5f056b39ccd5f0e18a840bd3204595d
https://doi.org/10.1145/3543829.3544537

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Creating Corpora for Seq2Seq Tone Rephrasing Using Social Media Posts

Autor: Paulo R. Cavalin, Marcelo Grave, Marisa Vasconcelos, Claudio S. Pinhanez

Publikováno v: IJCNN

We present a methodology to use Twitter posts to create a parallel corpus which can be used to train Seq2Seq neural networks for a tone rephrasing task. Given that people tend to post texts expressing opinions or emotions of varied intensities regard

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9be802ee8b8f5c48af543f51b451b53f
https://doi.org/10.1109/ijcnn48605.2020.9207298

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Influenza vaccination reduces all-cause mortality in patients with heart failure : a systematic review and meta-analysis of observational studies

Autor: Rodrigues, Bárbara Sucena Marcelo Grave

Publikováno v: Repositório Científico de Acesso Aberto de Portugal
Repositório Científico de Acesso Aberto de Portugal (RCAAP)
instacron:RCAAP

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2020 Submitted by Sofia Amador (sofiamador@fm.ul.pt) on 2021-02-23T14:04:43Z No. of bitstreams: 1 BarbaraSRodrigues.pdf: 996911 bytes, checksum:

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3056::f36dfdc569a2da1c9fd8d7c3ca234514

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From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer

Autor: Claudio S. Pinhanez, Paulo R. Cavalin, Marisa Vasconcelos, Victor Henrique Alves Ribeiro, Marcelo Grave

Publikováno v: EMNLP (Findings)

We present a method for creating parallel data to train Seq2Seq neural networks for sentiment transfer. Most systems for this task, which can be viewed as monolingual machine translation (MT), have relied on unsupervised methods, such as Generative A

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ac6ccc5ec5a6679c5bc4bfa9331a9341
https://doi.org/10.18653/v1/2020.findings-emnlp.61

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