121 citations · 197 across the 8 of their papers we have counts for
6 papers · 1 filter
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Khai Nguyen, Son Nguyen, Nhat Ho +2
Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the laten…
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Khai Nguyen, Nhat Ho, Tung Pham +1
Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability…
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Rui Shu, Hung H. Bui, Jay Whang +1
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up…
Amortized Inference Regularization
Rui Shu, Hung H. Bui, Shengjia Zhao +2
The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inf…
A DIRT-T Approach to Unsupervised Domain Adaptation
Rui Shu, Hung H. Bui, Hirokazu Narui +1
Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent…
Multilevel Clustering via Wasserstein Means
Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin +3
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a pote…