Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Ziletti, A."'
Fact Finder -- Enhancing Domain Expertise of Large Language Models by Incorporating Knowledge Graphs
Autor:
Steinigen, Daniel, Teucher, Roman, Ruland, Timm Heine, Rudat, Max, Flores-Herr, Nicolas, Fischer, Peter, Milosevic, Nikola, Schymura, Christopher, Ziletti, Angelo
Recent advancements in Large Language Models (LLMs) have showcased their proficiency in answering natural language queries. However, their effectiveness is hindered by limited domain-specific knowledge, raising concerns about the reliability of their
Externí odkaz:
http://arxiv.org/abs/2408.03010
Autor:
Ziletti, Angelo, D'Ambrosi, Leonardo
Publikováno v:
NAACL 2024 Clinical NLP Workshop
Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. Querying these databases to answer epidemiological questions is challenging due to the intricacy of med
Externí odkaz:
http://arxiv.org/abs/2403.09226
Autor:
Cécile Tréhin, Olivier Duriez, François Sarrazin, Benoit Betton, Jocelyn Fonderflick, Franziska Loercher, Etienne Marlé, Jean‐Francois Seguin, Julien Traversier, Noémie Ziletti, Jean‐Baptiste Mihoub
Publikováno v:
Ecosphere, Vol 15, Iss 8, Pp n/a-n/a (2024)
Abstract Restoring ecological dynamics is a key objective of conservation translocations. Exemplarily, reconnecting the reintroduced alpine populations with native Pyrenean populations through re‐establishing locally extinct populations in between,
Externí odkaz:
https://doaj.org/article/c74b814655c44ca58bcb106246611131
Autor:
Ziletti, Angelo, Akbik, Alan, Berns, Christoph, Herold, Thomas, Legler, Marion, Viell, Martina
Publikováno v:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
Medical coding (MC) is an essential pre-requisite for reliable data retrieval and reporting. Given a free-text reported term (RT) such as "pain of right thigh to the knee", the task is to identify the matching lowest-level term (LLT) - in this case "
Externí odkaz:
http://arxiv.org/abs/2206.02662
Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show t
Externí odkaz:
http://arxiv.org/abs/2109.01518
Publikováno v:
Leitherer, A., Ziletti, A. & Ghiringhelli, L.M. Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning. Nat. Commun. 12, 6234 (2021)
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major
Externí odkaz:
http://arxiv.org/abs/2103.09777
Autor:
Ziletti, Angelo, Berns, Christoph, Treichel, Oliver, Weber, Thomas, Liang, Jennifer, Kammerath, Stephanie, Schwaerzler, Marion, Virayah, Jagatheswari, Ruau, David, Ma, Xin, Mattern, Andreas
Publikováno v:
Front. Comput. Sci 88 (3) (2021)
Millions of unsolicited medical inquiries are received by pharmaceutical companies every year. It has been hypothesized that these inquiries represent a treasure trove of information, potentially giving insight into matters regarding medicinal produc
Externí odkaz:
http://arxiv.org/abs/2012.04545
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Sutton, Christopher, Ghiringhelli, Luca M., Yamamoto, Takenori, Lysogorskiy, Yury, Blumenthal, Lars, Hammerschmidt, Thomas, Golebiowski, Jacek, Liu, Xiangyue, Ziletti, Angelo, Scheffler, Matthias
Machine learning (ML) is increasingly used in the field of materials science, where statistical estimates of computed properties are employed to rapidly examine the chemical space for new compounds. However, a systematic comparison of several ML mode
Externí odkaz:
http://arxiv.org/abs/1812.00085
Publikováno v:
Nature Communications 9, 2775 (2018)
Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and analytics. Current
Externí odkaz:
http://arxiv.org/abs/1709.02298