158 citations · 460 across the 23 of their papers we have counts for
17 papers
Multiple instance learning with graph neural networks
Ming Tu, Jing Huang, Xiaodong He +1
Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN)…
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
Ming Tu, Guangtao Wang, Jing Huang +3
Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. I…
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…
Tensor Product Generation Networks for Deep NLP Modeling
Qiuyuan Huang, Paul Smolensky, Xiaodong He +2
We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding…
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Damien Teney, Peter Anderson, Xiaodong He +1
This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for r…
Submodular Mini-Batch Training in Generative Moment Matching Networks
Jun Qi
This article was withdrawn because (1) it was uploaded without the co-authors' knowledge or consent, and (2) there are allegations of plagiarism.