Deep Learning on Graphs: Methods and Applications
发布者：季洁发布时间：2019-10-25浏览次数：235
| Speaker: | Lingfei Wu, IBM AI Foundation Lab |
| Host: | Meiyue Shao, School of Data Science, Fudan University |
| Time: | 11:00-12:00, October 25, 2019 |
| Location: | Zibin N102, Fudan University |
| Abstract: | Recent years have seen a significantly growing amount of interests in graph neural networks (GNNs), especially on efforts devoted to developing more effective GNNs for node classification, graph classification, and graph generation. However, there are relatively less studies on other important topics such as graph-based encoder-decoder, deep graph matching, and deep graph learning. In the first part of the talk, I will introduce a Graph2Seq neural network framework, a novel attention-based encoder-decoder architecture for graph-to-sequence learning, and then talk about how to apply this model in different NLP tasks. In the second part of the talk, I will introduce a Hierarchical Graph Matching Network (HGMN) for computing the graph similarity between any pair of graph-structured objects. Our model jointly learns graph representations and a graph matching metric function for computing graph similarity in an end-to-end fashion. In the third part of the talk, I will introduce an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embeddings simultaneously. |
| Bio: | Dr. Lingfei Wu is a Research Staff Member in the IBM AI Foundations Labs, Ressoning group at IBM T. J. Watson Research Center. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Lingfei Wu is a passionate researcher and responsible team leader, developing novel deep learning/machine learning models for solving real-world challenging problems. He has served as the PI in IBM for several federal agencies such as DARPA and NSF (more than $1.8M), as well as MIT-IBM Watson AI Lab. He has published more than 50 top-ranked conference and journal papers in ML/DL/NLP domains and is a co-inventor of more than 20 filed US patents. He was the recipient of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC'19 and KDD workshop on DLG'19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, Venturebeat, TechTalks, SyncedReview, Leiphone, QbitAI, MIT News, IBM Research News, and SIAM News. He has organized or served as Poster co-chairs of IEEE BigData'19, Tutorial co-chairs of IEEE BigData'18, Workshop co-chairs of Deep Learning on Graphs (with KDD'19, IEEE BigData’19, and AAAI'20), and regularly served as a SPC/TPC member of the following major AI/ML/DL/DM/NLP conferences including NIPS, ICML, ICLR, ACL, IJCAI, AAAI, and KDD. |
