Zobrazeno 1 - 10
of 426
pro vyhledávání: '"Jauregi, P."'
Text classifiers are vulnerable to adversarial examples -- correctly-classified examples that are deliberately transformed to be misclassified while satisfying acceptability constraints. The conventional approach to finding adversarial examples is to
Externí odkaz:
http://arxiv.org/abs/2405.11904
Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predo
Externí odkaz:
http://arxiv.org/abs/2403.13240
Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays, cross-lingual text cl
Externí odkaz:
http://arxiv.org/abs/2306.04996
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are traine
Externí odkaz:
http://arxiv.org/abs/2203.02894
Autor:
David Cantero Burgos, Ekaitz Jauregi Iztueta, Inaki Maurtua Ormaechea, Jose Maria Martinez-Otzeta, Andrea Cabrera Mugica
Publikováno v:
IEEE Access, Vol 12, Pp 127862-127878 (2024)
Automotive Driver Assistance Systems (ADAS) applications are currently an intensive field of study and innovation. The development of an ADAS is a multidisciplinary task involving electronic hardware design, advanced software implementation, safety c
Externí odkaz:
https://doaj.org/article/178613e56fb94747acea2342f416ba30
Publikováno v:
PeerJ Analytical Chemistry, Vol 6, p e32 (2024)
Natural deep eutectic solvents (NADES) have emerged as an eco-friendly alternative for extracting bioactives, avoiding the use of flammable organic solvents and extreme temperatures and pH conditions. NADES rely on intermolecular interactions between
Externí odkaz:
https://doaj.org/article/894a54e4fbcf4dd2ab0a146cb5d60849
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has been added to train the models with an objective that is closer to thei
Externí odkaz:
http://arxiv.org/abs/2106.04080
Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recentl
Externí odkaz:
http://arxiv.org/abs/2106.02208
Autor:
Davide Bruno, Kristina M. Gicas, Ainara Jauregi‐Zinkunegi, Kimberly D. Mueller, Melissa Lamar
Publikováno v:
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, Vol 16, Iss 1, Pp n/a-n/a (2024)
Abstract We propose a novel method to assess delayed primacy in the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) memory test. We then examine whether this measure predicts post mortem Alzheimer's disease (AD) neuropathology in i
Externí odkaz:
https://doaj.org/article/b5886fc1b75d47fb8be80b97c4b89a72
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspect
Externí odkaz:
http://arxiv.org/abs/2010.03732