Výsledky vyhledávání - "Bogojeska, Jasmina"

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Combining Data Generation and Active Learning for Low-Resource Question Answering

Autor: Kimmich, Maximilian, Bartezzaghi, Andrea, Bogojeska, Jasmina, Malossi, Cristiano, Vu, Ngoc Thang

Neural approaches have become very popular in Question Answering (QA), however, they require a large amount of annotated data. In this work, we propose a novel approach that combines data augmentation via question-answer generation with Active Learni

Externí odkaz: http://arxiv.org/abs/2211.14880

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SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning

Autor: Atzeni, Mattia, Bogojeska, Jasmina, Loukas, Andreas

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs. In this paper, we address this issue by show

Externí odkaz: http://arxiv.org/abs/2110.14266

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Akademický článek

Do domain-specific protein language models outperform general models on immunology-related tasks?

Autor: Deutschmann, Nicolas, Pelissier, Aurelien, Weber, Anna, Gao, Shuaijun, Bogojeska, Jasmina, Martínez, María Rodríguez

Publikováno v: In ImmunoInformatics June 2024 14

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Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study

Autor: Roth, Jan A, Radevski, Gorjan, Marzolini, Catia, Rauch, Andri, Günthard, Huldrych F, Kouyos, Roger D, Fux, Christoph A, Scherrer, Alexandra U, Calmy, Alexandra, Cavassini, Matthias, Kahlert, Christian R, Bernasconi, Enos, Bogojeska, Jasmina, Battegay, Manuel

Publikováno v: Roth, Jan A; Radevski, Gorjan; Marzolini, Catia; Rauch, Andri; Günthard, Huldrych F; Kouyos, Roger D; Fux, Christoph A; Scherrer, Alexandra U; Calmy, Alexandra; Cavassini, Matthias; Kahlert, Christian R; Bernasconi, Enos; Bogojeska, Jasmina; Battegay, Manuel (2021). Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study. The journal of infectious diseases, 224(7), pp. 1198-1208. Oxford University Press 10.1093/infdis/jiaa236

BACKGROUND It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of co-morbidities in people living with HIV. METHODS In this proof-of-concept study, we included people l

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed47caf1f25cba88c0351d12463ed751

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Improving Low-Resource Question Answering using Active Learning in Multiple Stages

Autor: Schmidt, Maximilian, Bartezzaghi, Andrea, Bogojeska, Jasmina, Malossi, A. Cristiano I., Vu, Thang

Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::597db9ea5214a7dc353f5fb1802ce17f

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Evaluating pre-trained Sentence-BERT with class embeddings in active learning for multi-label text classification

Autor: Wertz, Lukas, Bogojeska, Jasmina, Mirylenka, Katsiaryna, Kuhn, Jonas

The Transformer Language Model is a powerful tool that has been shown to excel at various NLP tasks and has become the de-facto standard solution thanks to its versatility. In this study, we employ pre-trained document embeddings in an Active Learnin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::06f32296ddc75e850a760f4f933659aa

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