2015:

• 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]

2014:

• A. Dai, C. Olah, Q. V. Le
  Document Embedding with Paragraph Vectors
  NIPS Deep Learning Workshop, 2014. [PDF]

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

• Q.V. Le, T. Mikolov.
  Distributed Representations of Sentences and Documents
  ICML, 2014. [PDF],

2013:

• S. Bengio, J. Dean, D. Erhan, E. Ie, Q. Le, A. Rabinovich, J. Shlens, Y. Singer.
  Using Web Co-occurrence Statistics for Improving Image Categorization
  arXiv, 2013. [PDF],

• R. Socher, Q. V. Le, C.D. Manning, A.Y. Ng.
  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]

• T. Mikolov, Q.V. Le, I. Sutskever.
  Exploiting Similarities among Languages for Machine Translation.
  arXiv, 2013. [PDF], [Technology Review]

• T. Mikolov, I. Sutskever, Q.V. Le.
  Learning the meaning behind words.
  Google OpenSource Blogpost, 2013. [Link], [Popular press]

• 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]
  

2012:

• 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.
  Press: New York Times and others

• A.L. Maas, Q.V. Le, T.M. O'Neil, O. Vinyals, P. Nguyen, and A.Y. Ng.
  Recurrent Neural Networks for Noise Reduction in Robust ASR.
  Interspeech 2012. [Preprint]
  

• Q.V. Le, J. Han, J.W. Gray, P.T. Spellman, A. Borowsky, and B. Parvin
  Learning Invariant Features of Tumor Signatures.
  ISBI, 2012. [PDF]
  Topics: neural networks, unsupervised feature learning, RICA, computer vision

2011:

• Q.V. Le, A. Karpenko, J. Ngiam, A.Y. Ng.
  ICA with Reconstruction Cost for Efficient
  Overcomplete Feature Learning.
  NIPS, 2011. [PDF], [Appendix], [Code],
  Topics: neural networks, unsupervised feature learning, ICA, computer vision

• Q.V. Le, J. Ngiam, A. Coates, A. Lahiri, B. Prochnow, A.Y. Ng.
  On optimization methods for deep learning.
  ICML, 2011. [PDF], [Supplementary document], [More info]
  Topics: neural networks, optimization

• Q.V. Le, W.Y. Zou, S.Y. Yeung, A.Y. Ng.
  Learning hierarchical spatio-temporal features for action recognition
  with independent subspace analysis.
  CVPR, 2011. [PDF] (Oral presentation [Slides])
  [Appendix], [Code], [More Info].
  The code can let you train features on your unlabelled dataset.
  But if you do not want to train your features, you can
  download features on already trained on Hollywood2 [Features]
  Topics: neural networks, unsupervised learning, action recognition, invariances
  
  
• Quoc V. Le, Morgan Quigley, Jean Feng, Justin Chen, Will Y. Zou, Marc Rasi, Tiffany Low, Andrew Y. Ng
  Haptic Belt with Pedestrian Detection. NIPS, 2011 (Demonstrations). [Videos]

2010:

• Q.V. Le, J. Ngiam, Z. Chen, D. Chia, P. Koh, A.Y. Ng
  Tiled Convolutional Neural Networks. NIPS, 2010. [PDF]
  [TCNN code], [Invariance visualization].
  NIPS poster [PPT], [PNG]
  Topics: neural networks, unsupervised learning, Topographic ICA, object recognition.

• Q.V. Le, A. Saxena, A.Y. Ng
  Active Perception: Interactive Manipulation for Improving Object Detection. Technical Report, 2010. [PDF]
  Topics: computer vision, robotics.

• D. Rao, Q.V. Le, T. Phoka, M. Quigley, A. Sudsang, A.Y. Ng
  Grasping Novel Objects with Depth Segmentation IROS, 2010. [PDF] [Video]
  Topics: robotic grasping.

• M. Quigley, R. Brewer, S. P. Soudararaj, V. Pradeep, Q. Le, and A. Y. Ng
  Low-cost Accelerometers for Robotic Manipulator Perception. IROS, 2010 [PDF]
  Topics: robotic calibration, optimization, grasping.

• Q.V. Le, D. Kamm, A. Kara, and A. Y. Ng
  Learning to grasp objects with multiple contact points. ICRA, 2010. [PDF] [Video] [Slides]
  Topics: robotic grasping.

• C.H. Teo, S.V.N. Vishwanathan, A. Smola, Q.V. Le.
  Bundle Methods for Regularized Risk Minimization. JMLR, 2010. [PDF] [Code]
  Topics: learning and large-scale optimization.
  Notes: previous shorter versions appeared in KDD and NIPS with Appendix

2009:

• I.J. Goodfellow, Q.V. Le, A.M. Saxe, H. Lee, A.Y. Ng,
  Measuring invariances in deep networks. NIPS, 2009. [PDF]
  Topics: invariances, unsupervised learning, neural networks.

• A. Coates, P. Baumstarck, Q. Le, and A. Y. Ng
  Scalable Learning for Object Detection with GPU Hardware. IROS, 2009. [PDF]
  Topics: object detection, special hardware, GPUs.

• Q.V. Le, A.Y. Ng.
  Joint calibration of multiple sensors. IROS, 2009. [PDF]
  Topics: robotic sensor calibration.

• N. Quadrianto, A.J. Smola, T.S. Caetano, Q.V. Le
  Estimating Labels from Label Proportions. JMLR, 2009. [PDF]
  Topics: Gaussian Process classification, transduction, semi-supervised learning, prior knowledge.
  Notes: a previous shorter version appeared in ICML

• M. Quigley, S. Batra, S. Gould, E. Klingbeil, Q.V. Le, A. Wellman, A.Y. Ng.
  High Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening. ICRA, 2009. [PDF]
  Topics: 3D sensing hardware, object detection, manipulation.

• C.B. Do, Q.V. Le, C.S. Foo.
  Proximal regularization for online and batch learning. ICML, 2009. [PDF (conference version)] [PDF (extended version, with proofs)] [Code]
  Topics: learning and optimization.

• T.S. Caetano, J.J. McAuley, L. Cheng, Q.V. Le, A.J. Smola.
  Learning Graph Matching. PAMI, 2009. [PDF], [Digital library link], [Code]
  Topics: graph matching, structured estimation.

• C.B. Do, Q.V. Le, C.H. Teo, O. Chapelle, A.J. Smola.
  Tighter Bounds for Structured Estimation. NIPS 21, 2009. [PDF]
  Topics: structured estimation, computational biology, ranking.
  
  
• M. Quigley, S. Batra, S. Gould, E. Klingbeil, Q.V. Le, A. Y. Ng.
  High-Accuracy 3D Sensing for Mobile Manipulators. NIPS 21, 2009 (Demonstrations). [Videos], [Poster]

2008:

• N. Quadrianto, A. J. Smola, T. S. Caetano, Q. V. Le.
  Estimating Labels from Label Proportions. ICML, 2008. [PDF]
  Topics: Gaussian Process classification, transduction, semi-supervised learning, prior knowledge.

• M. Weimer, A. Karatzoglou, Q.V. Le, A.J. Smola.
  COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking. NIPS 20, 2008. [PDF] [Code/Website]
  Topics: ranking, collaborative-filtering, structured outputs, optimization.

• A.J. Smola, S.V.N. Vishwanathan, Q.V. Le.
  Bundle methods for machine learning. NIPS 20, 2008. [PDF] [Appendix], [NIPS spotlight], [Code]
  Topics: optimization, theory.

2007:

• C. Burges, R. Ragno, Q. V. Le.
  Learning to Rank with nonsmooth cost functions. NIPS 19, 2007. [PDF]
  Topics: ranking, neural networks.

• O. Chapelle, Q.V. Le, A.J. Smola.
  Large margin optimization of ranking measures. NIPS Workshop: Machine Learning for Web Search, 2007. [PDF][Updated technical report PDF (2009)]
  Topics: ranking, structured outputs.

• Q.V. Le, A.J. Smola.
  Direct optimization or ranking measures. NICTA Tech report, 2007. [PDF]
  Topics: ranking, structured outputs.

• T. Caetano, L. Cheng, Q.V. Le, A.J. Smola.
  Learning graph matching. ICCV, 2007. [PDF], [Oral presentation], [Code]
  Topics: graph matching, max-margin structured outputs, vision.

• C.H. Teo, Q.V. Le, A.J. Smola, SVN Vishwanathan.
  A Scalable Modular Convex Solver for Regularized Risk Minimization. KDD, 2007. [PDF], [Code]
  Topics: large-scale optimization, open-source software.

2005 - 2006:

• Q. V. Le, A. J. Smola, T. Gärtner, Y. Altun.
  Transductive Gaussian Process Regression with Automatic Model Selection. ECML, 2006. Best paper award, [PDF]
  Topics: Gaussian Process regression, transduction.

• Q. V. Le, A. J. Smola, T. Gärtner.
  Simpler knowledge-based Support Vector Machines. ICML, 2006. [PDF]
  Topics: prior knowledge, non-convex optimization.

• T. Gärtner, Q. V. Le, S. Burton, A. J. Smola, S. V. N. Vishwanathan.
  Large-Scale Multiclass Transduction. NIPS 18, 2006. [PDF]
  Topics: Gaussian Process classification, multiclass, transduction.

• Q. V. Le, A. J. Smola, S. Canu.
  Heteroscedastic Gaussian Process Regression. ICML, 2005. [PDF]
  Topics: Gaussian Process regression.

• I. Takeuchi, Q. V. Le, T. Sears, A. J. Smola.
  Nonparametric quantile estimation. JMLR 7, 2006. [old PDF], [PDF], [Code(ELEFANT)]
  Topics: quantile estimation, median estimation, theory.

• Q. V. Le, T. Sears, A. J. Smola.
  Nonparametric quantile estimation. NICTA Technical report, 2005. [PDF]
  Topics: quantile estimation, median estimation, theory.

• T. Gärtner, T. Horvath, Q. V. Le, A. J. Smola, S. Wrobel.
  Kernel Methods for Graphs. Mining Graph Data (Book chapter). L. Holder, D. Cook (editors), 2005.
  Topics: Gaussian Process classification, graphs.

• T. Gärtner, Q. V. Le, A. J. Smola.
  A Short Tour of Kernel Methods for Graphs. Tech report. 2006. [PDF]
  Topics: Gaussian Process classification, graphs.