generative model

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NeurIPS 2016 Generative Acoustic-Phonemic-Speaker Model Based on Three-Way Restricted Boltzmann Machine Interspeech 2016 Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data NeurIPS 2016 Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks JMLR 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Ladder Variational Autoencoders NeurIPS 2016 Learnable Visual Markers NeurIPS 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Learning Temporal Regularity in Video Sequences CVPR 2016 Learning to Generate with Memory ICML 2016 Modeling and Transforming Speech Using Variational Autoencoders Interspeech 2016 Neural Autoregressive Distribution Estimation JMLR 2016 Neural Variational Inference for Text Processing ICML 2016 Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics NeurIPS 2016 On Construction of Hybrid Logistic Regression-Naïve Bayes Model for Classification PGM 2016 Online Algorithms for Sum-Product Networks with Continuous Variables PGM 2016 PHOG: Probabilistic Model for Code ICML 2016 Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates NeurIPS 2016 Sequential Neural Models with Stochastic Layers NeurIPS 2016 Stochastic Neural Networks with Monotonic Activation Functions AISTATS 2016 Structured Generative Models of Continuous Features for Word Sense Induction COLING 2016 Texture Networks: Feed-forward Synthesis of Textures and Stylized Images ICML 2016 Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine JMLR 2016 Variational Inference for Monte Carlo Objectives ICML 2016 Wasserstein Training of Restricted Boltzmann Machines NeurIPS 2016