40 citations · 126 across the 23 of their papers we have counts for
15 papers · 1 filter
StructPrune: Structured Global Pruning asymptotics with GPU Memory
Xinyuan Song, Guangji Bai, Liang Zhao
Pruning is critical for scaling large language models (LLMs). Global pruning achieves strong performance but requires memory, which is infeasible for billion-param…
FedSpaLLM: Federated Pruning of Large Language Models
Guangji Bai, Yijiang Li, Zilinghan Li +2
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size,…
Structural Disentanglement of Causal and Correlated Concepts
Qilong Zhao, Shiyu Wang, Zeeshan Memon +5
Controllable data generation aims to synthesize data by specifying values for target concepts. Achieving this reliably requires modeling the underlying generative factors and their…
Continuous Temporal Domain Generalization
Zekun Cai, Guangji Bai, Renhe Jiang +2
Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus o…
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
Guangji Bai, Zheng Chai, Chen Ling +11
The burgeoning field of Large Language Models (LLMs), exemplified by sophisticated models like OpenAI's ChatGPT, represents a significant advancement in artificial intelligence. Th…
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
Junxiang Wang, Guangji Bai, Wei Cheng +3
Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classifica…
Saliency-Guided Hidden Associative Replay for Continual Learning
Guangji Bai, Qilong Zhao, Xiaoyang Jiang +2
Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning. While CL provides an edge ove…
Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction
Guangji Bai, Ziyang Yu, Zheng Chai +2
Despite the recent success of Graph Neural Networks (GNNs), it remains challenging to train GNNs on large-scale graphs due to neighbor explosions. As a remedy, distributed computin…
Domain Generalization Deep Graph Transformation
Shiyu Wang, Guangji Bai, Qingyang Zhu +2
Graph transformation that predicts graph transition from one mode to another is an important and common problem. Despite much progress in developing advanced graph transformation t…
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…
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…
Sign-regularized Multi-task Learning
Johnny Torres, Guangji Bai, Junxiang Wang +3
Multi-task learning is a framework that enforces different learning tasks to share their knowledge to improve their generalization performance. It is a hot and active domain that s…