1.1k citations · 2.8k across the 21 of their papers we have counts for
5 papers · 1 filter
A Tutorial on Deep Latent Variable Models of Natural Language
Yoon Kim, Sam Wiseman, Alexander M. Rush
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly…
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…
OpenNMT: Neural Machine Translation Toolkit
Guillaume Klein, Yoon Kim, Yuntian Deng +3
OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research in…
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…