[Seminar]MLDS Seminar 2023-2 by Mr. Haoyu Han (Michigan State Univ.), Mr. Weijie Liu (Zhejiang Univ.), Seminar Room L5DE23
Speaker 1: Mr. Haoyu Han, Ph. D. Student, Michigan State University
Title: Alternately Optimized Graph Neural Networks
Abstract: Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is often inefficient in computation and memory usage. In this work, we propose a new optimization framework for semi-supervised learning on graphs. The proposed framework can be conveniently solved by the alternating optimization algorithms, resulting in significantly improved efficiency. Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
Speaker 2: Mr. Weijie Liu, Ph. D. Student, Zhejiang University
Title: Robust Graph Dictionary Learning