Lingfei Wu
Lingfei Wu
Co-founder and CEO of Anytime.AI
Verified email at - Homepage
Cited by
Cited by
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Y Chen, L Wu, M Zaki
Advances in neural information processing systems 33, 19314-19326, 2020
A joint neural model for information extraction with global features
Y Lin, H Ji, F Huang, L Wu
Proceedings of the 58th annual meeting of the association for computational …, 2020
Improved code summarization via a graph neural network
A LeClair, S Haque, L Wu, C McMillan
Proceedings of the 28th international conference on program comprehension …, 2020
Graph neural networks: foundation, frontiers and applications
L Wu, P Cui, J Pei, L Zhao, X Guo
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022
Graph neural networks for natural language processing: A survey
L Wu, Y Chen, K Shen, X Guo, H Gao, S Li, J Pei, B Long
Foundations and Trends® in Machine Learning 16 (2), 119-328, 2023
Graph2seq: Graph to sequence learning with attention-based neural networks
K Xu, L Wu, Z Wang, Y Feng, M Witbrock, V Sheinin
arXiv preprint arXiv:1804.00823, 2018
Reinforcement learning based graph-to-sequence model for natural question generation
Y Chen, L Wu, MJ Zaki
arXiv preprint arXiv:1908.04942, 2019
Bidirectional attentive memory networks for question answering over knowledge bases
Y Chen, L Wu, MJ Zaki
arXiv preprint arXiv:1903.02188, 2019
Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward
L Huang, L Wu, L Wang
arXiv preprint arXiv:2005.01159, 2020
Quantized densely connected u-nets for efficient landmark localization
Z Tang, X Peng, S Geng, L Wu, S Zhang, D Metaxas
Proceedings of the European conference on computer vision (ECCV), 339-354, 2018
Word mover's embedding: From word2vec to document embedding
L Wu, IEH Yen, K Xu, F Xu, A Balakrishnan, PY Chen, P Ravikumar, ...
arXiv preprint arXiv:1811.01713, 2018
Improved automatic summarization of subroutines via attention to file context
S Haque, A LeClair, L Wu, C McMillan
Proceedings of the 17th International Conference on Mining Software …, 2020
Heterogeneous global graph neural networks for personalized session-based recommendation
Y Pang, L Wu, Q Shen, Y Zhang, Z Wei, F Xu, E Chang, B Long, J Pei
Proceedings of the fifteenth ACM international conference on web search and …, 2022
Reinforcement learning based text style transfer without parallel training corpus
H Gong, S Bhat, L Wu, JJ Xiong, W Hwu
arXiv preprint arXiv:1903.10671, 2019
Exploiting rich syntactic information for semantic parsing with graph-to-sequence model
K Xu, L Wu, Z Wang, M Yu, L Chen, V Sheinin
arXiv preprint arXiv:1808.07624, 2018
Robustness of graph neural networks at scale
S Geisler, T Schmidt, H Şirin, D Zügner, A Bojchevski, S Günnemann
Advances in Neural Information Processing Systems 34, 7637-7649, 2021
Discrete adversarial attacks and submodular optimization with applications to text classification
Q Lei, L Wu, PY Chen, A Dimakis, IS Dhillon, MJ Witbrock
Proceedings of Machine Learning and Systems 1, 146-165, 2019
Similarity preserving representation learning for time series clustering
Q Lei, J Yi, R Vaculin, L Wu, IS Dhillon
arXiv preprint arXiv:1702.03584, 2017
Sql-to-text generation with graph-to-sequence model
K Xu, L Wu, Z Wang, Y Feng, V Sheinin
arXiv preprint arXiv:1809.05255, 2018
Graphflow: Exploiting conversation flow with graph neural networks for conversational machine comprehension
Y Chen, L Wu, MJ Zaki
arXiv preprint arXiv:1908.00059, 2019
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