**All publications (Google Scholar version)** -- **Media/Awards** -- **Coauthors** -- **Professional services** -- **Cool robot videos**

**Infomation:**

Address: 1600 Amphitheatre Parkway Mountain View, CA 94043.

Advisor: Professor Andrew Ng.

Address: Room 110A, Gates Building, Stanford CA 94305.

Email:

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).

- 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