100 citations · 168 across the 7 of their papers we have counts for
3 papers · 2 filters
Avoiding Latent Variable Collapse With Generative Skip Models
Adji B. Dieng, Yoon Kim, Alexander M. Rush +1
Variational autoencoders learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the l…
Latent Alignment and Variational Attention
Yuntian Deng, Yoon Kim, Justin Chiu +2
Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method…
Semi-Amortized Variational Autoencoders
Yoon Kim, Sam Wiseman, Andrew C. Miller +2
Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network. While AVI has enabled efficient training of deep generative models…