Photo of Juan Ricardo Nanculef Alegria

Juan Ricardo Nanculef Alegria

  • 72 Citations
  • 4 h-Index
20042019

Research output per year

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Research Output

  • 72 Citations
  • 4 h-Index
  • 7 Paper
  • 3 Conference contribution
  • 3 Article
  • 1 Editorial

A Binary Variational Autoencoder for Hashing

Mena, F. & Ñanculef, R., 1 Jan 2019, A Binary Variational Autoencoder for Hashing. p. 131-141 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11896 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • BlackSheep: Dynamic effort estimation in agile software development using machine learning

    Mas’ad, R., Ñanculef, R. & Astudillo, H., 1 Jan 2019, p. 16-29. 14 p.

    Research output: Contribution to conferencePaper

  • Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations

    Mena, F. & Ñanculef, R., 1 Jan 2019, Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations. p. 493-503 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11896 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Efficient sparse approximation of support vector machines solving a kernel Lasso

    Aliquintuy, M., Frandi, E., Ñanculef, R. & Suykens, J. A. K., 1 Jan 2017, Efficient sparse approximation of support vector machines solving a kernel Lasso. p. 208-216 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10125 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Proceedings - International Conference of the Chilean Computer Science Society, SCCC

    Codocedo, V., Nanculef, R. & Cubillos, C., 27 Jan 2017.

    Research output: Contribution to conferenceEditorial