Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lucas Deckers"'
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
Spiking neural network (SNN) distinguish themselves from artificial neural network (ANN) because of their inherent temporal processing and spike-based computations, enabling a power-efficient implementation in neuromorphic hardware. In this study, we
Externí odkaz:
https://doaj.org/article/624dbb4adfe846878f52a87d5899a778
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
A liquid state machine (LSM) is a biologically plausible model of a cortical microcircuit. It exists of a random, sparse reservoir of recurrently connected spiking neurons with fixed synapses and a trainable readout layer. The LSM exhibits low traini
Externí odkaz:
https://doaj.org/article/bf13ca09ba91412487a106315593b9d1
Autor:
Servaas Vandecappelle, Lucas Deckers, Neetha Das, Amir Hossein Ansari, Alexander Bertrand, Tom Francart
Publikováno v:
eLife, Vol 10 (2021)
In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker s
Externí odkaz:
https://doaj.org/article/77707970900c4f2292636949a9b97f8a
Autor:
Lucas Deckers, Neetha Das, Tom Francart, Servaas Vandecappelle, Alexander Bertrand, Amir Hossein Ansari
Publikováno v:
eLife
eLife, Vol 10 (2021)
eLife, Vol 10 (2021)
In a multi-speaker scenario, the human auditory system is able to attend to one particular speaker of interest and ignore the others. It has been demonstrated that it is possible to use electroencephalography (EEG) signals to infer to which speaker s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b1a2e781849f5676755f9c9a4762b7e
https://doi.org/10.1101/475673
https://doi.org/10.1101/475673
Autor:
Alison O'Neil, Aneta Lisowska, Lucas Deckers, Matúš Falis, Patrick Schrempf, Shadia Mikhael, Maciej Pajak, Sotirios A. Tsaftaris
Publikováno v:
Scopus-Elsevier
LOUHI@EMNLP
University of St Andrews CRIS
LOUHI@EMNLP
University of St Andrews CRIS
We present a semantically interpretable system for automated ICD coding of clinical text documents. Our contribution is an ontological attention mechanism which matches the structure of the ICD ontology, in which shared attention vectors are learned
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6263d1a30ddf0270df77d7ad8fd0fe22
http://www.scopus.com/inward/record.url?eid=2-s2.0-85119384836&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85119384836&partnerID=MN8TOARS