Models

Generative Image Modeling Using Spatial LSTMs NeurIPS 2015 Generative Moment Matching Networks ICML 2015 Geometry and Expressive Power of Conditional Restricted Boltzmann Machines JMLR 2015 Grammar as a Foreign Language NeurIPS 2015 Kullback-Leibler Proximal Variational Inference NeurIPS 2015 Learning Deep Sigmoid Belief Networks with Data Augmentation AISTATS 2015 Learning Structured Output Representation using Deep Conditional Generative Models NeurIPS 2015 Learning to Generate Chairs With Convolutional Neural Networks CVPR 2015 Learning to Linearize Under Uncertainty NeurIPS 2015 Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines ICCV 2015 Local Expectation Gradients for Black Box Variational Inference NeurIPS 2015 MADE: Masked Autoencoder for Distribution Estimation ICML 2015 Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015 Max-Margin Deep Generative Models NeurIPS 2015 On Learning Optimized Reaction Diffusion Processes for Effective Image Restoration CVPR 2015 On some provably correct cases of variational inference for topic models NeurIPS 2015 On Theoretical Properties of Sum-Product Networks AISTATS 2015 Pointer Networks NeurIPS 2015 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015 Regularizing Max-Margin Exemplars by Reconstruction and Generative Models CVPR 2015 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process AISTATS 2015 Scalable Variational Gaussian Process Classification AISTATS 2015 Scalable Variational Inference in Log-supermodular Models ICML 2015 Sensor Selection for Crowdsensing Dynamical Systems AISTATS 2015 Similarity-based Contrastive Divergence Methods for Energy-based Deep Learning Models ACML 2015