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All publications by Andrew Y. Ng
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Deep Learning of Invariant Features via Simulated Fixations in Video
Will Zou, Shenghuo Zhu, Kai Yu and Andrew Y. Ng
Advances in Neural Information Processing Systems 25, 2012


Emergence of Object-Selective Features in Unsupervised Feature Learning
Adam Coates, Andrej Karpathy and Andrew Y. Ng
Advances in Neural Information Processing Systems 25, 2012


Large Scale Distributed Deep Networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le and Andrew Y. Ng
Advances in Neural Information Processing Systems 25, 2012


Convolutional-Recursive Deep Learning for 3D Object Classification
Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning and Andrew Y. Ng
Advances in Neural Information Processing Systems 25, 2012


Building high-level features using large scale unsupervised learning
Marc'aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg Corrado, Jeff Dean, Quoc V. Le and Andrew Y. Ng
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


Selecting Receptive Fields in Deep Networks
Adam Coates and Andrew Y. Ng
Advances in Neural Information Processing Systems 24, 2011


Unsupervised learning models of primary cortical receptive fields and receptive field plasticity
Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe and Andrew Y. Ng
Advances in Neural Information Processing Systems 24, 2011


Sparse Filtering
Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh and Andrew Y. Ng
Advances in Neural Information Processing Systems 24, 2011


ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam and Andrew Y. Ng
Advances in Neural Information Processing Systems 24, 2011


Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning and Andrew Y. Ng
Advances in Neural Information Processing Systems 24, 2011


Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher, Cliff C. Lin, Chris Manning and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


On Random Weights and Unsupervised Feature Learning
Andrew Saxe, Pang W. Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Learning Deep Energy Models
Jiquan Ngiam, Zhenghao Chen, Pang W. Koh and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


Multimodal Deep Learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


On optimization methods for deep learning
Jiquan Ngiam, Adam Coates, Ahbik Lahiri, Bobby Prochnow, Quoc V. Le and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization
Adam Coates and Andrew Y. Ng
Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011


An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Andrew Y. Ng and Honglak Lee
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Energy Disaggregation via Discriminative Sparse Coding
J. Z. Kolter, Siddharth Batra and Andrew Y. Ng
Advances in Neural Information Processing Systems 23, 2010


Tiled convolutional neural networks
Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang W. Koh, Quoc V. Le and Andrew Y. Ng
Advances in Neural Information Processing Systems 23, 2010


Large-scale deep unsupervised learning using graphics processors
Rajat Raina, Anand Madhavan and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Regularization and feature selection in least-squares temporal difference learning
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Near-Bayesian exploration in polynomial time
J. Z. Kolter and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


A majorization-minimization algorithm for (multiple) hyperparameter learning
Chuan-sheng Foo, Chuong B. Do and Andrew Y. Ng
Proceedings of the 26th International Conference on Machine Learning (ICML-09), 2009


Unsupervised feature learning for audio classification using convolutional deep belief networks
Honglak Lee, Peter Pham, Yan Largman and Andrew Y. Ng
Advances in Neural Information Processing Systems 22, 2009


Measuring Invariances in Deep Networks
Ian Goodfellow, Honglak Lee, Quoc V. Le, Andrew Saxe and Andrew Y. Ng
Advances in Neural Information Processing Systems 22, 2009


Space-indexed dynamic programming: learning to follow trajectories
J. Z. Kolter, Adam Coates, Andrew Y. Ng, Yi Gu and Charles Duhadway
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Learning for control from multiple demonstrations
Adam Coates, Pieter Abbeel and Andrew Y. Ng
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer and Andrew Y. Ng
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion
J. Z. Kolter, Pieter Abbeel and Andrew Y. Ng
Advances in Neural Information Processing Systems 20, 2007


Sparse deep belief net model for visual area V2
Honglak Lee, Chaitanya Ekanadham and Andrew Y. Ng
Advances in Neural Information Processing Systems 20, 2007


Efficient multiple hyperparameter learning for log-linear models
Chuan-sheng Foo, Chuong B. Do and Andrew Y. Ng
Advances in Neural Information Processing Systems 20, 2007


Constructing informative priors using transfer learning
Rajat Raina, Andrew Y. Ng and Daphne Koller
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Using inaccurate models in reinforcement learning
Pieter Abbeel, Morgan Quigley and Andrew Y. Ng
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Efficient sparse coding algorithms
Honglak Lee, Alexis Battle, Rajat Raina and Andrew Y. Ng
Advances in Neural Information Processing Systems 19, 2006


An Application of Reinforcement Learning to Aerobatic Helicopter Flight
Pieter Abbeel, Adam Coates, Morgan Quigley and Andrew Y. Ng
Advances in Neural Information Processing Systems 19, 2006


Map-Reduce for Machine Learning on Multicore
Cheng-tao Chu, Sang K. Kim, Yi-an Lin, Yuanyuan Yu, Gary Bradski, Kunle Olukotun and Andrew Y. Ng
Advances in Neural Information Processing Systems 19, 2006


Robotic Grasping of Novel Objects
Ashutosh Saxena, Justin Driemeyer, Justin Kearns and Andrew Y. Ng
Advances in Neural Information Processing Systems 19, 2006


Learning Factor Graphs in Polynomial Time and Sample Complexity
Pieter Abbeel, Daphne Koller and Andrew Y. Ng
Journal of Machine Learning Research, 2006


High speed obstacle avoidance using monocular vision and reinforcement learning
Jeff Michels, Ashutosh Saxena and Andrew Y. Ng
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Exploration and apprenticeship learning in reinforcement learning
Pieter Abbeel and Andrew Y. Ng
Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005


Learning vehicular dynamics, with application to modeling helicopters
Pieter Abbeel, Varun Ganapathi and Andrew Y. Ng
Advances in Neural Information Processing Systems 18, 2005


Fast Gaussian Process Regression using KD-Trees
Yirong Shen, Matthias Seeger and Andrew Y. Ng
Advances in Neural Information Processing Systems 18, 2005


Learning Depth from Single Monocular Images
Ashutosh Saxena, Sung H. Chung and Andrew Y. Ng
Advances in Neural Information Processing Systems 18, 2005


On Local Rewards and Scaling Distributed Reinforcement Learning
Drew Bagnell and Andrew Y. Ng
Advances in Neural Information Processing Systems 18, 2005


Learning random walk models for inducing word dependency distributions
Kristina Toutanova, Christopher D. Manning and Andrew Y. Ng
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Online and batch learning of pseudo-metrics
Shai Shalev-shwartz, Yoram Singer and Andrew Y. Ng
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


Learning first-order Markov models for control
Pieter Abbeel and Andrew Y. Ng
Advances in Neural Information Processing Systems 17, 2004


Learning Syntactic Patterns for Automatic Hypernym Discovery
Rion Snow, Daniel Jurafsky and Andrew Y. Ng
Advances in Neural Information Processing Systems 17, 2004


Online Bounds for Bayesian Algorithms
Sham M. Kakade and Andrew Y. Ng
Advances in Neural Information Processing Systems 17, 2004


Stable adaptive control with online learning
H. J. Kim and Andrew Y. Ng
Advances in Neural Information Processing Systems 17, 2004


Policy Search by Dynamic Programming
J. A. Bagnell, Sham M. Kakade, Jeff G. Schneider and Andrew Y. Ng
Advances in Neural Information Processing Systems 16, 2003


Autonomous Helicopter Flight via Reinforcement Learning
H. J. Kim, Michael I. Jordan, Shankar Sastry and Andrew Y. Ng
Advances in Neural Information Processing Systems 16, 2003


Classification with Hybrid Generative/Discriminative Models
Rajat Raina, Yirong Shen, Andrew Mccallum and Andrew Y. Ng
Advances in Neural Information Processing Systems 16, 2003


Latent Dirichlet Allocation
David M. Blei, Andrew Y. Ng and Michael I. Jordan
Journal of Machine Learning Research, 2003


Distance Metric Learning with Application to Clustering with Side-Information
Eric P. Xing, Michael I. Jordan, Stuart Russell and Andrew Y. Ng
Advances in Neural Information Processing Systems 15, 2002


Latent Dirichlet Allocation
David M. Blei, Andrew Y. Ng and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001


On Spectral Clustering: Analysis and an algorithm
Andrew Y. Ng, Michael I. Jordan and Yair Weiss
Advances in Neural Information Processing Systems 14, 2001


On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes
Andrew Y. Ng and Michael I. Jordan
Advances in Neural Information Processing Systems 14, 2001