← Learning Types
Machine Learning › Learning Types ›

Generative Models

197 directly classified papers

Papers per year

Papers

Tensor Monte Carlo: Particle Methods for the GPU era NIPS 2019
The Implicit Metropolis-Hastings Algorithm NIPS 2019
Practical Full Resolution Learned Lossless Image Compression CVPR 2019
Neural Linguistic Steganography EMNLP 2019
Max-Sliced Wasserstein Distance and Its Use for GANs CVPR 2019
Mixture Density Generative Adversarial Networks CVPR 2019
Multivariate Time Series Imputation with Generative Adversarial Networks NIPS 2018
Banach Wasserstein GAN NIPS 2018
Constrained Graph Variational Autoencoders for Molecule Design NIPS 2018
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models NIPS 2017
Filtering Variational Objectives NIPS 2017
Wasserstein Learning of Deep Generative Point Process Models NIPS 2017
Causal Effect Inference with Deep Latent-Variable Models NIPS 2017
Inferring Generative Model Structure with Static Analysis NIPS 2017
Population Matching Discrepancy and Applications in Deep Learning NIPS 2017
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization NIPS 2016
Regularizing Max-Margin Exemplars by Reconstruction and Generative Models CVPR 2015
A Generative Model for Parts-based Object Segmentation NIPS 2012
Unsupervised Structure Discovery for Semantic Analysis of Audio NIPS 2012
Free energy score space NIPS 2009
Evaluating probabilities under high-dimensional latent variable models NIPS 2008
Generative versus discriminative training of RBMs for classification of fMRI images NIPS 2008