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  • 2013 Conference - NeurIPS
    Video lectures has posted the videos from the 2013 NIPS Workshops held in Lake Tahoe View Videos Speakers Richard Wilkinson, The University of Nottingham Judea Pearl and Elias Bareinboim, UCLA Rob The NeurIPS Logo above may be used on presentations Right-click and choose download
  • Conference on Neural Information Processing Systems
    From 1987 until 2000 NeurIPS was held in Denver, United States Since then, the conference was held in Vancouver, Canada (2001–2010), Granada, Spain (2011), and Lake Tahoe, United States (2012–2013)
  • dblp: Neural Information Processing Systems 2013
    Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013 Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States 2013
  • 2013 - NIPS
    2013 at the age of 36 Ben moved in the spring of 2013 from the University of Pennsylvania to the University of Washington as Boeing Associate Professor He worked in machine learning, computational linguistics and computer vision and his research on structured prediction won best paper awards at NIPS and EMNLP conferences
  • NEURAL INFORMATION PROCESSING SYSTEMS. ANNUAL CONFERENCE. 27TH 2013. (4 . . .
    Date Location: Held 5-10 December 2013, Lake Tahoe, Nevada, USA
  • NIPS 2013 Call For Papers - NeurIPS
    The 2013 conference will be held on December 5-8 at Lake Tahoe, Nevada One day of tutorials (December 5) will precede the main conference, and two days of workshops (December 9-10) will follow it at the same location
  • NeurIPS
    The NeurIPS Logo above may be used on presentations Right-click and choose download It is a vector graphic and may be used at any scale
  • NeurIPS 2021 Fact Sheet 35th Annual Conference of Neural Information . . .
    Year: NeurIPS 2010 Authors: Matthew Hoffman, David Blei and Francis Bach Presented: Saturday, December 11 at 01:00 GMT (Friday, December 10 at 5:00 pm PST) in the final session of the conference About: This paper introduces a stochastic variational gradient based inference


















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