40 citations · 126 across the 23 of their papers we have counts for
6 papers · 1 filter
Saliency-Augmented Memory Completion for Continual Learning
Guangji Bai, Chen Ling, Yuyang Gao +1
Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic…
Deep Spatial Domain Generalization
Dazhou Yu, Guangji Bai, Yun Li +1
Spatial autocorrelation and spatial heterogeneity widely exist in spatial data, which make the traditional machine learning model perform badly. Spatial domain generalization is a…
Saliency-Regularized Deep Multi-Task Learning
Guangji Bai, Liang Zhao
Multitask learning is a framework that enforces multiple learning tasks to share knowledge to improve their generalization abilities. While shallow multitask learning can learn tas…
RES: A Robust Framework for Guiding Visual Explanation
Yuyang Gao, Tong Steven Sun, Guangji Bai +3
Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research que…
Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization
Zheng Chai, Guangji Bai, Liang Zhao +1
Despite the recent success of Graph Neural Networks, it remains challenging to train a GNN on large graphs with millions of nodes and billions of edges, which are prevalent in many…
Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks
Guangji Bai, Chen Ling, Liang Zhao
Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen…