- Auto-Encoding Variational Bayes. ICLR 2014
- A Recurrent Latent Variable Model for Sequential Data. NIPS 2015
- beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. ICLR 2017
- Neural Discrete Representation Learning
- Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. ICML 2019
- NVAE: A Deep Hierarchical Variational Autoencoder. NeurIPS 2020
- Variational Inference with Normalizing Flows. ICML 2015
- NICE: Non-linear Independent Components Estimation. ICLR 2015
- Improving Variational Auto-Encoders using Householder Flow. NIPS 2016
- Density Estimation using Real NVP. ICLR 2017
- Glow: Generative Flow with Invertible 1x1 Convolutions. NeurIPS 2018
- Conditional Flow Variational Autoencoders for Structured Sequence Prediction. NeurIPS 2019
- PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows. ICCV 2019
- Structured Output Learning with Conditional Generative Flows. AAAI 2020
- C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds. CVPR 2020
- Poisson Flow Generative Models. NeurIPS 2022
- Denoising Diffusion Probabilistic Models. NeurIPS 2020
- Denoising Diffusion Implicit Models. ICLR 2021
- Improved Denoising Diffusion Probabilistic Models. ICML 2021
- High-Resolution Image Synthesis with Latent Diffusion Models. CVPR 2022
- Diffusion Autoencoders: Toward a Meaningful and Decodable Representation. CVPR 2022