22 citations · 69 across the 8 of their papers we have counts for
9 papers
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Pengyu Cheng, Martin Renqiang Min, Dinghan Shen +4
Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Si…
Dynamic Embedding on Textual Networks via a Gaussian Process
Pengyu Cheng, Yitong Li, Xinyuan Zhang +3
Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures;…
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin J Liang, Guoyin Wang, Yitong Li +2
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally. By consid…
LMVP: Video Predictor with Leaked Motion Information
Dong Wang, Yitong Li, Wei Cao +3
We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly pr…
StoryGAN: A Sequential Conditional GAN for Story Visualization
Yitong Li, Zhe Gan, Yelong Shen +6
We propose a new task, called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast…
Diffusion Maps for Textual Network Embedding
Xinyuan Zhang, Yitong Li, Dinghan Shen +1
Textual network embedding leverages rich text information associated with the network to learn low-dimensional vectorial representations of vertices. Rather than using typical natu…
Multi-Label Learning from Medical Plain Text with Convolutional Residual Models
Xinyuan Zhang, Ricardo Henao, Zhe Gan +2
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label cl…
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…