142 citations · 405 across the 18 of their papers we have counts for
18 papers
Ask Questions with Double Hints: Visual Question Generation with Answer-awareness and Region-reference
Kai Shen, Lingfei Wu, Siliang Tang +4
The visual question generation (VQG) task aims to generate human-like questions from an image and potentially other side information (e.g. answer type). Previous works on VQG fall…
Feeding What You Need by Understanding What You Learned
Xiaoqiang Wang, Bang Liu, Fangli Xu +3
Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-si…
Life span of solutions to a PDE model for Lithium-ion batteries in high space dimensions
Xiangsheng Xu
In this paper we study a system of partial differential equations which models lithium-ion batteries. The system describes the conservation of Lithium and conservation of charges i…
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks
Hanning Gao, Lingfei Wu, Hongyun Zhang +4
Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence mod…
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Hanning Gao, Lingfei Wu, Po Hu +3
Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods fi…
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Qi Shen, Lingfei Wu, Yitong Pang +4
Predicting the next interaction of a short-term sequence is a challenging task in session-based recommendation (SBR).Multi-behavior session recommendation considers session sequenc…
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
Yiming Zhang, Lingfei Wu, Qi Shen +5
Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-…
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Yitong Pang, Lingfei Wu, Qi Shen +6
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation. Almost all existing works rely on item transition pattern…
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Yangkai Du, Tengfei Ma, Lingfei Wu +4
Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct…
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Hanlu Wu, Tengfei Ma, Lingfei Wu +1
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-ex…
Deep Graph Matching and Searching for Semantic Code Retrieval
Xiang Ling, Lingfei Wu, Saizhuo Wang +6
Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses na…
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Xiang Ling, Lingfei Wu, Saizhuo Wang +5
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph…
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem
Shucheng Li, Lingfei Wu, Shiwei Feng +3
The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem s…
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…
Word Mover's Embedding: From Word2Vec to Document Embedding
Lingfei Wu, Ian E. H. Yen, Kun Xu +5
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised…
Random Warping Series: A Random Features Method for Time-Series Embedding
Lingfei Wu, Ian En-Hsu Yen, Jinfeng Yi +3
Time series data analytics has been a problem of substantial interests for decades, and Dynamic Time Warping (DTW) has been the most widely adopted technique to measure dissimilari…
Scalable Spectral Clustering Using Random Binning Features
Lingfei Wu, Pin-Yu Chen, Ian En-Hsu Yen +3
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems d…
D2KE: From Distance to Kernel and Embedding
Lingfei Wu, Ian En-Hsu Yen, Fangli Xu +2
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more natura…