Models

Deep Learning Models of the Retinal Response to Natural Scenes NeurIPS 2016 Deep Structured Energy Based Models for Anomaly Detection ICML 2016 Disease Trajectory Maps NeurIPS 2016 Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation AISTATS 2016 d-VMP: Distributed Variational Message Passing PGM 2016 Dynamic Sum Product Networks for Tractable Inference on Sequence Data PGM 2016 Early Stopping as Nonparametric Variational Inference AISTATS 2016 Factored Temporal Sigmoid Belief Networks for Sequence Learning ICML 2016 f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NeurIPS 2016 Generating Images with Perceptual Similarity Metrics based on Deep Networks NeurIPS 2016 Generating Videos with Scene Dynamics NeurIPS 2016 Generative Acoustic-Phonemic-Speaker Model Based on Three-Way Restricted Boltzmann Machine Interspeech 2016 Generative Adversarial Text to Image Synthesis ICML 2016 Hierarchical Variational Models ICML 2016 How to Center Deep Boltzmann Machines JMLR 2016 Improved Techniques for Training GANs NeurIPS 2016 Improved Variational Inference with Inverse Autoregressive Flow NeurIPS 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Ladder Variational Autoencoders NeurIPS 2016 Language Identification Based on Generative Modeling of Posteriorgram Sequences Extracted from Frame-by-Frame DNNs and LSTM-RNNs Interspeech 2016 Learnable Visual Markers NeurIPS 2016 Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling NeurIPS 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Learning to Generate with Memory ICML 2016 Learning What and Where to Draw NeurIPS 2016