All publications (Google Scholar version) -- Almost complete bibfile -- Media Coverage-- Coauthors -- Professional services -- Cool robot videos
Current (2013-2014): Research Scientist, Google.
Recent publications: (All publications)
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).
Addressing the Rare Word Problem in Neural Machine Translation
arXiv 2014. [PDF]
Sequence to Sequence Learning with Neural Networks
NIPS 2014. [PDF]
Distributed Representations of Sentences and Documents
ICML, 2014. [PDF],
Using Web Co-occurrence Statistics for Improving Image Categorization
arXiv, 2013. [PDF],
Grounded Compositional Semantics for Finding and Describing Images with Sentences.
Transactions of the Association for Computational Linguistics (TACL 2013).
also at: Deep Learning Workshop at NIPS 2013. [PDF]
Exploiting Similarities among Languages for Machine Translation.
arXiv, 2013. [PDF], [Technology Review]
Learning the meaning behind words.
Google OpenSource Blogpost, 2013. [Link], [Popular press]
Fastfood — Approximating Kernel Expansions in Loglinear Time.
ICML, 2013. [PDF], [PDF with Supplementary]
On Rectified Linear Units for Speech Processing.
IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP), 2013. [PDF]
Large Scale Distributed Deep Networks.
NIPS, 2012. [PDF], [Project page]
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].
Randomized Methods for Machine Learning.
Deep Learning and Unsupervised Feature Learning, NIPS'2012.
Challenges in Learning Hierarchical Models: Transfer Learning and Optimization.