217 citations · 2.1k across the 107 of their papers we have counts for
15 papers · 1 filter
Deep Iterative and Adaptive Learning for Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph s…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
KerGM: Kernelized Graph Matching
Zhen Zhang, Yijian Xiang, Lingfei Wu +2
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assign…
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang +5
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global propert…
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio +4
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. I…
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. In this paper, we propose a novel reinforcement learning (RL) based graph-to-sequence (Gra…
MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics
Fangli Xu, Lingfei Wu, KP Thai +3
Automatic analysis of teacher and student interactions could be very important to improve the quality of teaching and student engagement. However, despite some recent progress in u…
Multi-stage Deep Classifier Cascades for Open World Recognition
Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu +4
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more…
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
Yuyang Gao, Lingfei Wu, Houman Homayoun +1
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of th…
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii)…
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However…
Attacking Graph Convolutional Networks via Rewiring
Yao Ma, Suhang Wang, Tyler Derr +2
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural…
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
Hongyu Gong, Suma Bhat, Lingfei Wu +2
Text style transfer rephrases a text from a source style (e.g., informal) to a target style (e.g., formal) while keeping its original meaning. Despite the success existing works ha…
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Yu Chen, Lingfei Wu, Mohammed J. Zaki
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based meth…
High-Fidelity Vector Space Models of Structured Data
Maxwell Crouse, Achille Fokoue, Maria Chang +6
Machine learning systems regularly deal with structured data in real-world applications. Unfortunately, such data has been difficult to faithfully represent in a way that most mach…