All publications (Google Scholar version) -- Almost complete bibfile -- Media Coverage-- Coauthors -- Professional services -- Cool robot videos
Infomation:
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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 quocle and somewhere is stanford.edu
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).
- Q.V. Le, T. Sarlos, A.J. Smola.
Fastfood — Approximating Kernel Expansions in Loglinear Time.
ICML, 2013. [PDF], [PDF with Supplementary]
- M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, G. Hinton.
On Rectified Linear Units for Speech Processing.
IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP), 2013. [PDF]
- J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M.A. Ranzato, A. Senior, P. Tucker, K. Yang, A. Y. Ng.
Large Scale Distributed Deep Networks.
NIPS, 2012. [PDF], [Project page]
- Q.V. Le, M.A. Ranzato, R. Monga, M. Devin, K. Chen, G.S. Corrado, J. Dean, A.Y. Ng.
Building high-level features using large scale unsupervised learning..
ICML, 2012. [PDF], [Project page]
(Large scale deep learning simulations on 10000s of cores that lead to:
- Face and cat neurons from unlabeled data,
- State-of-the-art on ImageNet from raw pixels.)
Topics: Large-scale deep learning, computer vision.
Google Official Blog Post, Google Research G+ post
Press: New York Times (front page), NPR, BBC, the Atlantic, MSNBC, the Economist, Times and many others
Slides: [hour-long talk], [20-minute talk].
- 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).
- Yoshua Bengio, James Bergstra, and I organized a workshop at NIPS'2012:
Deep Learning and Unsupervised Feature Learning, NIPS'2012.
- Me at Kioloa.
- STAIR project
