Yoshua Bengio

Yoshua Bengio
Born 1964 (age 5253)
France
Residence Montreal, Quebec
Citizenship Canada
Fields Computer science
Institutions Université de Montréal
Alma mater McGill University
Thesis Artificial Neural Networks and their Application to Sequence Recognition (1991)
Doctoral advisor Renato de Mori
Notable students Ian Goodfellow,Hugo Larochelle,Pascal Vincent,Nicolas Chapados,Kyunghyun Cho,Antoine Bordes,Aaron Courville,Samy Bengio,
Known for Deep Learning,Neural machine translation,Generative Adversarial Networks,Word embeddings,Denoising Auto-Encoders,neural language models
Website
iro.umontreal.ca/~bengioy

Yoshua Bengio (born 1964 in France) is a Canadian computer scientist, most noted for his work on artificial neural networks and deep learning.[1][2][3]

Bengio received his Bachelor of Science (electrical engineering), Master of Engineering (computer science) and PhD (computer science) from McGill University.[4] He was a post-doctoral fellow at MIT (under Michael I. Jordan) and AT&T Bell Labs.[5] Bengio has been a faculty member at the Université de Montréal since 1993, heads the MILA (Montreal Institute for Learning Algorithms) and is co-director of the Learning in Machines & Brains project of the Canadian Institute for Advanced Research.[4][5]

References

  1. Knight, Will (July 9, 2015). "IBM Pushes Deep Learning with a Watson Upgrade". MIT Technology Review. Retrieved July 31, 2016.
  2. LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature. 521 (7553): 436–444. PMID 26017442. doi:10.1038/nature14539.
  3. Bergen, Mark; Wagner, Kurt (July 15, 2015). "Welcome to the AI Conspiracy: The 'Canadian Mafia' Behind Tech's Latest Craze". Recode. Retrieved July 31, 2016.
  4. 1 2 "Yoshua Bengio". Profiles. Canadian Institute For Advanced Research. Retrieved July 31, 2016.
  5. 1 2 Bengio, Yoshua. "CV". Département d'informatique et de recherche opérationnelle. Université de Montréal. Retrieved July 31, 2016.


This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.