Quoc V. Le

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Infomation:

Current (2013-Present): Research Scientist, Google.
Address: 1600 Amphitheatre Parkway Mountain View, CA 94043.

Past: PhD Student, AI Lab, Computer Science Department, Stanford University.
Advisor: Professor Andrew Ng.
Address: Room 110A, Gates Building, Stanford CA 94305.
Email: someone@somewhere where someone is quoc.leviet and somewhere is gmail.com
I did my undergraduate at ANU & NICTA (Canberra, Australia), under the supervision of Professor Alex Smola.
I was also a research visitor at Dept Schölkopf, Max Planck Institute for Biological Cybernetics (Tübingen, Germany).

• A. Neelakantan, L. Vilnis, Q.V. Le, I. Sutskever, L. Kaiser, K. Kurach, J. Martens
  Adding Gradient Noise Improves Learning for Very Deep Networks
  arXiv, 2015. [PDF]
  

• M.T. Luong, Q.V. Le, I. Sutskever, O. Vinyals, L. Kaiser
  Multitask Sequence to Sequence Learning
  arXiv, 2015. [PDF]
  

• A. Neelakantan, Q.V. Le, I. Sutskever
  Neural Programmer: Inducing Latent Programs With Gradient Descent
  arXiv, 2015. [PDF]
  

• N. Jaitly, Q.V. Le, O. Vinyals, I. Sutskever, S. Bengio
  An Online Sequence-to-Sequence Model Using Partial Conditioning
  arXiv, 2015. [PDF]
  

• A. Dai, Q.V. Le
  Semi-supervised Sequence Learning
  NIPS, 2015. [PDF]
  

• Q.V. Le
  A Tutorial on Deep Learning
  Lecture Notes, 2015.
  Part 1: Nonlinear Classifiers and The Backpropagation Algorithm,
  Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks
  Videos and Descriptions (courtesy of Gaurav Trivedi)

• W. Chan, N. Jaitly, Q.V. Le, O. Vinyals
  Listen, Attend and Spell
  arXiv 2015. [PDF]

• O. Vinyals, Q.V. Le
  A Neural Conversational Model
  ICML Deep Learning Workshop, arXiv 2015. [PDF]

• Q. V. Le, N. Jaitly, G. E. Hinton
  A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
  arXiv, 2015. [PDF]

• T.M. Luong, I. Sutskever, Q.V. Le, O. Vinyals, W. Zaremba
  Addressing the Rare Word Problem in Neural Machine Translation
  ACL 2015. [PDF]

• I. Sutskever, O. Vinyals, Q. V. Le
  Sequence to Sequence Learning with Neural Networks
  NIPS 2014. [PDF]

• My 3-hour lectures on deep learning. My notes are divided into two parts: Part1 and Part 2.
• Code for training deep autoencoder with L-BFGS (See this paper; this implementation is not optimized for speed)
• Stacked ISA for Videos (state-of-the-art video features, see our CVPR 2011 paper)
• TCNN code (improving convolutional neural networks with untied weights).
• Invariance visualization (visualization techniques for optimal stimuli and inviariances).
• Proximal optimization (stabilizing online gradient descent, such as Pegasos, with proximity).
• Some code is in ELEFANT or also in MLOSS (a general machine learning toolbox for kernel methods, graphical models, decision trees etc.)
• Some other is in BMRM (probably one of the first Map-Reduce style machine learning toolboxes).
• CoFiRank (a large scale collaborative ranking toolbox).
• I previously contributed to ROS (Robot Operating System).

• David Lopez-Paz, Alex Smola and I organized a workshop at NIPS'2013:
  Randomized Methods for Machine Learning.
  
• Yoshua Bengio, James Bergstra, and I organized a workshop at NIPS'2012:
  Deep Learning and Unsupervised Feature Learning, NIPS'2012.
• Marc'Aurelio Ranzato, Ruslan Salakhutdinov, Andrew Y. Ng and Josh Tenenbaum and I organized a workshop at NIPS'2011:
  Challenges in Learning Hierarchical Models: Transfer Learning and Optimization.
  
  

• Me at Kioloa.
• STAIR project