34 citations · 102 across the 12 of their papers we have counts for
6 papers · 1 filter
Towards Robust Graph Incremental Learning on Evolving Graphs
Junwei Su, Difan Zou, Zijun Zhang +1
Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a…
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training
Borui Wan, Juntao Zhao, Chuan Wu
Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and emb…
Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment
Shiwei Zhang, Xiaodong Yi, Lansong Diao +3
This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- het…
On the Limitation and Experience Replay for GNNs in Continual Learning
Junwei Su, Difan Zou, Chuan Wu
Continual learning seeks to empower models to progressively acquire information from a sequence of tasks. This approach is crucial for many real-world systems, which are dynamic an…
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing
Tianfeng Liu, Yangrui Chen, Dan Li +7
Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data, achieving ground-breaking performance on various tasks such as no…
Adversarial Deep Learning for Online Resource Allocation
Bingqian Du, Zhiyi Huang, Chuan Wu
Online algorithm is an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usual…