Deep Learning

Structured Determinantal Point Processes NeurIPS 2010 Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010 The Maximal Causes of Natural Scenes are Edge Filters NeurIPS 2010 Tight Sample Complexity of Large-Margin Learning NeurIPS 2010 Tiled convolutional neural networks NeurIPS 2010 Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models NeurIPS 2010 Understanding the difficulty of training deep feedforward neural networks AISTATS 2010 Unsupervised Kernel Dimension Reduction NeurIPS 2010 Variable margin losses for classifier design NeurIPS 2010 Variational bounds for mixed-data factor analysis NeurIPS 2010 Variational Inference over Combinatorial Spaces NeurIPS 2010 Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010 3D Object Recognition with Deep Belief Nets NeurIPS 2009 Abstraction and Relational learning NeurIPS 2009 A Gaussian Tree Approximation for Integer Least-Squares NeurIPS 2009 A Neural Implementation of the Kalman Filter NeurIPS 2009 An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism NeurIPS 2009 A Stochastic approximation method for inference in probabilistic graphical models NeurIPS 2009 Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing NeurIPS 2009 AUC optimization and the two-sample problem NeurIPS 2009 Bootstrapping from Game Tree Search NeurIPS 2009 Code-specific policy gradient rules for spiking neurons NeurIPS 2009 Conditional Neural Fields NeurIPS 2009 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation NeurIPS 2009 Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models NeurIPS 2009