Zobrazeno 1 - 10
of 3 411
pro vyhledávání: '"A. Mosteiro"'
Autor:
D. Santana Moreno, L. Llull, A. Mosteiro, C. Laredo, L. Pedrosa, R. Mellado, G. Pujol, R. Torné, S. Amaro
Publikováno v:
Neurology Perspectives, Vol 4, Iss , Pp 108- (2024)
Externí odkaz:
https://doaj.org/article/6cc3ba7b4e4f403c92663e30ab8059d4
Autor:
T.E. Topczewski, R. Torne, L.A. Reyes, L. Pedrosa, A. Ferres, A. Mosteiro, L. Gomez, M. Codes, J. Enseñat
Publikováno v:
Brain and Spine, Vol 3, Iss , Pp 102244- (2023)
Externí odkaz:
https://doaj.org/article/0e728eeab5fe4a46b4909f9bdad40825
Various benchmarks have been proposed to test linguistic understanding in pre-trained vision \& language (VL) models. Here we build on the existence task from the VALSE benchmark (Parcalabescu et al, 2022) which we use to test models' understanding o
Externí odkaz:
http://arxiv.org/abs/2407.10488
Autor:
Garcia-Garcia, Sergio, Cepeda, Santiago, Muller, Dominik, Mosteiro, Alejandra, Torne, Ramon, Agudo, Silvia, de la Torre, Natalia, Arrese, Ignacio, Sarabia, Rosario
PURPOSE: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN), a form of deep learning, are capable of generating highly accurate predictions from imaging data. Our objective was to predict mor
Externí odkaz:
http://arxiv.org/abs/2308.13373
Autor:
Belz, Anya, Thomson, Craig, Reiter, Ehud, Abercrombie, Gavin, Alonso-Moral, Jose M., Arvan, Mohammad, Braggaar, Anouck, Cieliebak, Mark, Clark, Elizabeth, van Deemter, Kees, Dinkar, Tanvi, Dušek, Ondřej, Eger, Steffen, Fang, Qixiang, Gao, Mingqi, Gatt, Albert, Gkatzia, Dimitra, González-Corbelle, Javier, Hovy, Dirk, Hürlimann, Manuela, Ito, Takumi, Kelleher, John D., Klubicka, Filip, Krahmer, Emiel, Lai, Huiyuan, van der Lee, Chris, Li, Yiru, Mahamood, Saad, Mieskes, Margot, van Miltenburg, Emiel, Mosteiro, Pablo, Nissim, Malvina, Parde, Natalie, Plátek, Ondřej, Rieser, Verena, Ruan, Jie, Tetreault, Joel, Toral, Antonio, Wan, Xiaojun, Wanner, Leo, Watson, Lewis, Yang, Diyi
We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include th
Externí odkaz:
http://arxiv.org/abs/2305.01633
Publikováno v:
In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 271-281, Seattle, United States. Association for Computational Linguistics
This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relatio
Externí odkaz:
http://arxiv.org/abs/2210.03378
Attribute-based encryption (ABE) comprises a set of one-to-many encryption schemes that allow the encryption and decryption of data by associating it with access policies and attributes. Therefore, it is an asymmetric encryption scheme, and its compu
Externí odkaz:
http://arxiv.org/abs/2209.12742
Publikováno v:
Building and Environment, vol. 237, June 2023, p. 110318
Before 2020, the way occupants utilized the built environment had been changing slowly towards scenarios in which occupants have more choice and flexibility in where and how they work. The global COVID-19 pandemic accelerated this phenomenon rapidly
Externí odkaz:
http://arxiv.org/abs/2210.06124
Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model trained on c
Externí odkaz:
http://arxiv.org/abs/2205.12093
Autor:
Borger, Thomas, Mosteiro, Pablo, Kaya, Heysem, Rijcken, Emil, Salah, Albert Ali, Scheepers, Floortje, Spruit, Marco
Publikováno v:
Expert Systems with Applications Volume 199, 1 August 2022, 116720
Inpatient violence is a common and severe problem within psychiatry. Knowing who might become violent can influence staffing levels and mitigate severity. Predictive machine learning models can assess each patient's likelihood of becoming violent bas
Externí odkaz:
http://arxiv.org/abs/2205.10234