← Models
Deep Learning › Models ›

Generative Models

6381 directly classified papers

Papers per year

Papers

Priors for Diversity in Generative Latent Variable Models NIPS 2012
Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis ACML 2012
A Generative Model for Parts-based Object Segmentation NIPS 2012
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding NIPS 2012
A Better Way to Pretrain Deep Boltzmann Machines NIPS 2012
Multimodal Learning with Deep Boltzmann Machines NIPS 2012
Cardinality Restricted Boltzmann Machines NIPS 2012
Hierarchical spike coding of sound NIPS 2012
Discriminative Learning of Sum-Product Networks NIPS 2012
Deep Boltzmann Machines as Feed-Forward Hierarchies AISTATS 2012
Image Denoising and Inpainting with Deep Neural Networks NIPS 2012
Robust Bayesian Matrix Factorisation AISTATS 2011
Learning to Learn with Compound HD Models NIPS 2011
Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability NIPS 2011
Expressive Power and Approximation Errors of Restricted Boltzmann Machines NIPS 2011
On Tracking The Partition Function NIPS 2011
Mixed-Variate Restricted Boltzmann Machines ACML 2011
Discussion of “The Neural Autoregressive Distribution Estimator” AISTATS 2011
Generative Kernels for Exponential Families AISTATS 2011
Robust Generation of Dynamical Patterns in Human Motion by a Deep Belief Nets ACML 2011
The Neural Autoregressive Distribution Estimator AISTATS 2011
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines NIPS 2011
Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities NIPS 2011
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning NIPS 2011
Two Distributed-State Models For Generating High-Dimensional Time Series JMLR 2011