55 citations · 83 across the 2 of their papers we have counts for
3 papers
cs.LG2019★ 55 cited
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)…
cs.CL2019★ 28 cited
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…
cs.LG2016
Reducing the Model Order of Deep Neural Networks Using Information Theory
Ming Tu, Visar Berisha, Yu Cao +1
Deep neural networks are typically represented by a much larger number of parameters than shallow models, making them prohibitive for small footprint devices. Recent research shows…