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pro vyhledávání: '"Slezak, Diego Fernandez"'
Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effe
Externí odkaz:
http://arxiv.org/abs/2301.00792
Recent research has shown that static word embeddings can encode word frequency information. However, little has been studied about this phenomenon and its effects on downstream tasks. In the present work, we systematically study the association betw
Externí odkaz:
http://arxiv.org/abs/2211.08203
Measuring human capabilities to synchronize in time, adapt to perturbations to timing sequences or reproduce time intervals often require experimental setups that allow recording response times with millisecond precision. Most setups present auditory
Externí odkaz:
http://arxiv.org/abs/2105.01570
Autor:
Valentini, Francisco, Rosati, Germán, Blasi, Damián, Slezak, Diego Fernandez, Altszyler, Edgar
In recent years, word embeddings have been widely used to measure biases in texts. Even if they have proven to be effective in detecting a wide variety of biases, metrics based on word embeddings lack transparency and interpretability. We analyze an
Externí odkaz:
http://arxiv.org/abs/2104.06474
Automatic segmentation of white matter hyperintensities in magnetic resonance images is of paramount clinical and research importance. Quantification of these lesions serve as a predictor for risk of stroke, dementia and mortality. During the last ye
Externí odkaz:
http://arxiv.org/abs/2009.04985
Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in
Externí odkaz:
http://arxiv.org/abs/1903.03445
Akademický článek
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Publikováno v:
Proceedings of the 3rd Workshop on Representation Learning for NLP, pages 1-10, 2018, ACL
Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present
Externí odkaz:
http://arxiv.org/abs/1712.10054
Autor:
Mota, Natalia Bezerra, Pinheiro, Sylvia, Sigman, Mariano, Slezak, Diego Fernandez, Cecchi, Guillermo, Copelli, Mauro, Ribeiro, Sidarta
Discourse varies with age, education, psychiatric state and historical epoch, but the ontogenetic and cultural dynamics of discourse structure remain to be quantitatively characterized. To this end we investigated word graphs obtained from verbal rep
Externí odkaz:
http://arxiv.org/abs/1612.09268
Publikováno v:
Conscious Cogn. 2017 Nov;56:178-187
Word embeddings have been extensively studied in large text datasets. However, only a few studies analyze semantic representations of small corpora, particularly relevant in single-person text production studies. In the present paper, we compare Skip
Externí odkaz:
http://arxiv.org/abs/1610.01520