@prefix ltk: <http://data.loterre.fr/ark:/67375/LTK> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix ns0: <http://www.ebi.ac.uk/swo/> .
@prefix dc: <http://purl.org/dc/terms/> .

ltk:-R1C8NPZ1-K
  skos:prefLabel "Neural networks models"@en, "modèles de réseaux de neurones"@fr ;
  a skos:Concept ;
  skos:narrower ltk:-PZ0FQSKJ-2 .

ltk:-HQ28SCKT-2
  skos:prefLabel "PyTorch-NLP"@en, "PyTorch-NLP"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-ZN7C5RR5-L
  skos:prefLabel "convBERTurk"@en, "conBERTurk"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-JFGVSQX2-Q
  skos:prefLabel "HDLTex"@en, "HDLTex"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-X0Q4C3T5-8
  skos:prefLabel "OpenNRE"@en, "OpenNRE"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-PZ0FQSKJ-2
  ns0:SWO_0000085 ltk:-JX88X65X-R, ltk:-T1MXK7ZD-Q, ltk:-X0Q4C3T5-8, ltk:-ZN7C5RR5-L, ltk:-HQ28SCKT-2, ltk:-B33WWVFL-Z, ltk:-JFGVSQX2-Q ;
  skos:altLabel "réseau neuronal convolutif"@fr, "ConvNet"@en, "ConvNet"@fr, "CNN"@en, "CNN"@fr, "réseau de neurones à convolution"@fr ;
  skos:inScheme ltk: ;
  dc:bibliographicCitation "• LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., & Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network. In Proceedings Advances in Neural Information Processing Systems, 396‑404.", "• LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., & Jackel, L. D. (1989.). Handwritten digit recognition with a back-propagation network. Proceedings of the 2nd International Conference on Neural Information Processing Systems  (pp. 396-404).", "• Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278‑2324.  <a href=\"https://doi.org/10.1109/5.726791\">https://doi.org/10.1109/5.726791</a>", "• Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232‑243.  <a href=\"https://doi.org/10.1016/j.matcom.2020.04.031\">https://doi.org/10.1016/j.matcom.2020.04.031</a>" ;
  a skos:Concept ;
  skos:broader ltk:-R1C8NPZ1-K ;
  skos:definition "un réseau de neurones convolutifs (ConvNet) est une classe de réseau de neurones profonds, le plus souvent appliqué pour analyser l'imagerie visuelle. (Wikipedia, récupéré le 02/07/2021)."@fr, "a convolutional neural network (ConvNet) is a class of deep neural network, most commonly applied to analyze visual imagery.(Wikipedia, retrieved on 2021/07/02)."@en ;
  skos:prefLabel "convolutional neural network"@en, "réseau de neurones convolutif"@fr .

ltk:-B33WWVFL-Z
  skos:prefLabel "TextAttack"@en, "TextAttack"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-JX88X65X-R
  skos:prefLabel "LangMoDHS"@fr, "LangMoDHS"@en ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk:-T1MXK7ZD-Q
  skos:prefLabel "convBERT"@en, "convBERT"@fr ;
  a skos:Concept ;
  ns0:SWO_0000740 ltk:-PZ0FQSKJ-2 .

ltk: a skos:ConceptScheme .