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.