159 citations · 910 across the 32 of their papers we have counts for
6 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…
Parametric t-Distributed Stochastic Exemplar-centered Embedding
Martin Renqiang Min, Hongyu Guo, Dinghan Shen
Parametric embedding methods such as parametric t-SNE (pt-SNE) have been widely adopted for data visualization and out-of-sample data embedding without further computationally expe…
Video Generation From Text
Yitong Li, Martin Renqiang Min, Dinghan Shen +2
Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract bo…
Learning Context-Sensitive Convolutional Filters for Text Processing
Dinghan Shen, Martin Renqiang Min, Yitong Li +1
Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employ…
Deconvolutional Latent-Variable Model for Text Sequence Matching
Dinghan Shen, Yizhe Zhang, Ricardo Henao +2
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical opt…
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…