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of 153
pro vyhledávání: '"P, Vernikos"'
Neural machine translation systems estimate probabilities of target sentences given source sentences, yet these estimates may not align with human preferences. This work introduces QE-fusion, a method that synthesizes translations using a quality est
Externí odkaz:
http://arxiv.org/abs/2401.06688
Autor:
Wolleb, Benoist, Silvestri, Romain, Vernikos, Giorgos, Dolamic, Ljiljana, Popescu-Belis, Andrei
Subword tokenization is the de facto standard for tokenization in neural language models and machine translation systems. Three advantages are frequently cited in favor of subwords: shorter encoding of frequent tokens, compositionality of subwords, a
Externí odkaz:
http://arxiv.org/abs/2306.01393
Autor:
Vernikos, Giorgos, Bražinskas, Arthur, Adamek, Jakub, Mallinson, Jonathan, Severyn, Aliaksei, Malmi, Eric
Despite the impressive performance of large language models (LLMs), they often lag behind specialized models in various tasks. LLMs only use a fraction of the existing training data for in-context learning, while task-specific models harness the full
Externí odkaz:
http://arxiv.org/abs/2305.13514
We hypothesize that existing sentence-level machine translation (MT) metrics become less effective when the human reference contains ambiguities. To verify this hypothesis, we present a very simple method for extending pretrained metrics to incorpora
Externí odkaz:
http://arxiv.org/abs/2209.13654
State-of-the-art multilingual systems rely on shared vocabularies that sufficiently cover all considered languages. To this end, a simple and frequently used approach makes use of subword vocabularies constructed jointly over several languages. We hy
Externí odkaz:
http://arxiv.org/abs/2109.04556
Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we prop
Externí odkaz:
http://arxiv.org/abs/2109.03764
In Natural Language Processing (NLP), pretrained language models (LMs) that are transferred to downstream tasks have been recently shown to achieve state-of-the-art results. However, standard fine-tuning can degrade the general-domain representations
Externí odkaz:
http://arxiv.org/abs/2009.13366
Rehabilitation assisted by Space technology—A SAHC approach in immobilized patients—A case of stroke
Autor:
Chrysoula Kourtidou-Papadeli, Christos Frantzidis, Ilias Machairas, Christos Giantsios, Emmanouil Dermitzakis, Nikolaos Kantouris, Evdokimos Konstantinids, Panagiotis Bamidis, Joan Vernikos
Publikováno v:
Frontiers in Physiology, Vol 13 (2023)
Introduction: The idea behind the presentation of this case relates to utilizing space technology in earth applications with mutual benefit for both patients confined to bed and astronauts. Deconditioning and the progressiveness of skeletal muscle lo
Externí odkaz:
https://doaj.org/article/77dcf0bd74f94345aff924af66c19ffb
Publikováno v:
Sensors, Vol 23, Iss 10, p 4899 (2023)
The presence of occlusion in human activity recognition (HAR) tasks hinders the performance of recognition algorithms, as it is responsible for the loss of crucial motion data. Although it is intuitive that it may occur in almost any real-life enviro
Externí odkaz:
https://doaj.org/article/d92e574136a1465eb92e31009be681a3
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