371 citations · 3.9k across the 110 of their papers we have counts for
9 papers · 1 filter
Topic Compositional Neural Language Model
Wenlin Wang, Zhe Gan, Wenqi Wang +5
We propose a Topic Compositional Neural Language Model (TCNLM), a novel method designed to simultaneously capture both the global semantic meaning and the local word ordering struc…
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang +4
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. Wi…
Adversarial Symmetric Variational Autoencoder
Yunchen Pu, Weiyao Wang, Ricardo Henao +4
A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: () from observed data fed thr…
Deconvolutional Paragraph Representation Learning
Yizhe Zhang, Dinghan Shen, Guoyin Wang +3
Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a…
Triangle Generative Adversarial Networks
Zhe Gan, Liqun Chen, Weiyao Wang +5
A Triangle Generative Adversarial Network (-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each…
Adversarial Feature Matching for Text Generation
Yizhe Zhang, Zhe Gan, Kai Fan +4
The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with…
Stochastic Gradient Monomial Gamma Sampler
Yizhe Zhang, Changyou Chen, Zhe Gan +2
Recent advances in stochastic gradient techniques have made it possible to estimate posterior distributions from large datasets via Markov Chain Monte Carlo (MCMC). However, when t…
VAE Learning via Stein Variational Gradient Descent
Yunchen Pu, Zhe Gan, Ricardo Henao +3
A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make para…
Character-level Deep Conflation for Business Data Analytics
Zhe Gan, P. D. Singh, Ameet Joshi +4
Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a databa…