NIPS Proceedingsβ

Andrew Y. Ng

38 Papers

  • Convolutional-Recursive Deep Learning for 3D Object Classification (2012)
  • Deep Learning of Invariant Features via Simulated Fixations in Video (2012)
  • Emergence of Object-Selective Features in Unsupervised Feature Learning (2012)
  • Large Scale Distributed Deep Networks (2012)
  • Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection (2011)
  • ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning (2011)
  • Selecting Receptive Fields in Deep Networks (2011)
  • Sparse Filtering (2011)
  • Unsupervised learning models of primary cortical receptive fields and receptive field plasticity (2011)
  • Energy Disaggregation via Discriminative Sparse Coding (2010)
  • Tiled convolutional neural networks (2010)
  • Measuring Invariances in Deep Networks (2009)
  • Unsupervised feature learning for audio classification using convolutional deep belief networks (2009)
  • Efficient multiple hyperparameter learning for log-linear models (2007)
  • Hierarchical Apprenticeship Learning with Application to Quadruped Locomotion (2007)
  • Sparse deep belief net model for visual area V2 (2007)
  • An Application of Reinforcement Learning to Aerobatic Helicopter Flight (2006)
  • Efficient sparse coding algorithms (2006)
  • Map-Reduce for Machine Learning on Multicore (2006)
  • Robotic Grasping of Novel Objects (2006)
  • Fast Gaussian Process Regression using KD-Trees (2005)
  • Learning Depth from Single Monocular Images (2005)
  • Learning vehicular dynamics, with application to modeling helicopters (2005)
  • On Local Rewards and Scaling Distributed Reinforcement Learning (2005)
  • Learning first-order Markov models for control (2004)
  • Learning Syntactic Patterns for Automatic Hypernym Discovery (2004)
  • Online Bounds for Bayesian Algorithms (2004)
  • Stable adaptive control with online learning (2004)
  • Autonomous Helicopter Flight via Reinforcement Learning (2003)
  • Classification with Hybrid Generative/Discriminative Models (2003)
  • Policy Search by Dynamic Programming (2003)
  • Distance Metric Learning with Application to Clustering with Side-Information (2002)
  • Latent Dirichlet Allocation (2001)
  • On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes (2001)
  • On Spectral Clustering: Analysis and an algorithm (2001)
  • Approximate Inference A lgorithms for Two-Layer Bayesian Networks (1999)
  • Approximate Planning in Large POMDPs via Reusable Trajectories (1999)
  • Policy Search via Density Estimation (1999)