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Poster
Learning to Propagate for Graph Meta-Learning
LU LIU · Tianyi Zhou · Guodong Long · Jing Jiang · Chengqi Zhang

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #43

Meta-learning extracts the common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data, e.g., few-shot learning. In most meta-learning methods, tasks are implicitly related by sharing parameters or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can significantly improve few-shot learning. The graph’s structure is usually free or cheap to obtain but has rarely been explored in previous works. We develop a novel meta-learner of this type for prototype based classification, in which a prototype is generated for each class, such that the nearest neighbor search among the prototypes produces an accurate classification. The meta-learner, called “Gated Propagation Network (GPN)”, learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class benefits from the data of other related classes. In GPN, an attention mechanism aggregates messages from neighboring classes of each class, with a gate choosing between the aggregated message and the message from the class itself. We train GPN on a sequence of tasks from many-shot to few-shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, under different training-test discrepancy and test task generation settings, GPN outperforms recent meta-learning methods on two benchmark datasets. Code of GPN is publicly available at: https://github.com/liulu112601/Gated-Propagation-Net.

Author Information

LU LIU (University of Technology Sydney)

Lu Liu is a 3-rd year Ph.D. student from University of Technology Sydney. Her research interests lie in Machine Learning, Meta-learning and Low-shot learning.

Tianyi Zhou (University of Washington, Seattle)

Tianyi Zhou is a 6th-year Ph.D student of Paul G. Allen School of Computer Science and Engineering at University of Washington, Seattle, supervised by Jeff Bilmes and Carlos Guestrin. He has worked with Dacheng Tao at University of Technology Sydney and Nanyang Technological University for 4 years before going to UW. His research covers topics in machine learning, natural language processing, statistics, and data analysis. He has published 30+ papers with 1300+ citations at top conferences and journals including NeurIPS, ICML, ICLR, AISTATS, NAACL, ACM SIGKDD, IEEE ICDM, AAAI, IJCAI, IEEE ISIT, Machine Learning Journal (Springer), DMKD (Springer), IEEE TIP, IEEE TNNLS, etc. He is the recipient of the best student paper award at IEEE ICDM 2013.

Guodong Long (University of Technology Sydney (UTS))
Jing Jiang (University of Technology Sydney)
Chengqi Zhang (University of Technology Sydney)

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