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Deep Learning Reading Group

Introduction

Deep Learning reading group started its work at the Summer of 2014. The main focus of this group is to understand the theoretical basis as well as the applications of Deep Learning methods in Machine Learning and Computer Vision. Each week we focus on a particular paper or research and gather to discuss the work and its aspects.
In order to join the Deep Learning group and receive its emails send "subscribe deeplearning youremail" to majordomo[@]cs.ubc.ca.
We meet every Wednesday, 11:00 to 12:30 @ ICCS 304.

Update

Starting Sept 2014, we have merged our group with the Machine Learning Reading Group.

Papers

  1. Aug 27, 2014: Sharan Vaswani presents: Sanjeev Arora et al.. "Provable Bounds for Learning Some Deep Representations" JMLR. 2014
    [arXiv (Long)] [JMLR (Short)]
  2. Aug 13, 2014: Gabriel Goh presents: Benjamin Recht et al.. "Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent" NIPS. 2011
    [NIPS Page]
  3. Aug 13, 2014: Alireza Shafaei presents: Jeffrey Dean et al.. "Large Scale Distributed Deep Networks" NIPS. 2012
    [NIPS Page]
  4. July 30, 2014: Bobak Shahriari presents: Misha Denil et al.. "Predicting Parameters in Deep Learning" NIPS. 2013
    [NIPS Page]
  5. July 23, 2014: Alireza Shafaei presents: Matthew D. Zeiler and Rob Fergus. "Visualizing and Understanding Convolutional Networks" CoRR abs/1311.2901. 2013
    [arXiv] [SLIDES]
  6. July 14, 2014: Ankur Gupta and Sharan Vaswani present: Sutskever, Ilya, James Martens, and Geoffrey E. Hinton. "Generating text with recurrent neural networks" Proceedings of the ICML. 2011
    [PDF]
  7. July 9, 2014: Ives Macedo presents: Sutskever, Ilya, et al. "On the importance of initialization and momentum in deep learning." Proceedings of the ICML-13. 2013
    [PDF]
  8. July 2, 2014: Sharan Vaswani presents: Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint arXiv:1312.6199 (2013)
    [arXiv]
  9. June 25, 2014: Alireza Shafaei presents: Tompson, Jonathan, et al. "Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation." arXiv preprint arXiv:1406.2984 (2014)
    [arXiv] [SLIDES]
  10. June 18, 2014: Gabriel Goh presents: Hinton et. al. "Improving neural networks by preventing co-adaptation of feature detectors"
    [arXiv]
  11. June 18, 2014: Alireza Shafaei presents: Bengio, Deep Learning Book, Convolutional Networks chapter.
    [DRAFT]
  12. June 11, 2014: Gabriel Goh presents: Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.
    [PDF]
  13. June 4, 2014: Neil Traft presents: Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends in Machine Learning 2.1 (2009): 1-127.
    Chapters 6, 7. [PDF] [SLIDES]
  14. May 28, 2014: Ankur Gupta and Sharan Vaswani present: Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends in Machine Learning 2.1 (2009): 1-127.
    Chapters 4, 5. [PDF] [SLIDES]
  15. May 21, 2014: Alireza Shafaei presents: Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends in Machine Learning 2.1 (2009): 1-127.
    Chapters 1, 2, 3. [PDF] [SLIDES]