40 citations · 126 across the 23 of their papers we have counts for
24 papers
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…
Cross-modal RAG: Sub-dimensional Text-to-Image Retrieval-Augmented Generation
Mengdan Zhu, Senhao Cheng, Guangji Bai +2
Text-to-image generation increasingly demands access to domain-specific, fine-grained, and rapidly evolving knowledge that pretrained models cannot fully capture, necessitating the…
Continuous Domain Generalization
Zekun Cai, Yiheng Yao, Guangji Bai +4
Real-world data distributions often shift continuously across multiple latent factors such as time, geography, and socioeconomic contexts. However, existing domain generalization a…
MEGL: Multimodal Explanation-Guided Learning
Yifei Zhang, Tianxu Jiang, Bo Pan +3
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification.…
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…
Uncertainty Quantification for In-Context Learning of Large Language Models
Chen Ling, Xujiang Zhao, Xuchao Zhang +10
In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) and revolutionized various fields by providing a few task-relevant demonstrations in the…
SparseLLM: Towards Global Pruning for Pre-trained Language Models
Guangji Bai, Yijiang Li, Chen Ling +2
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has eme…
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…
Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations
Yifei Zhang, James Song, Siyi Gu +4
Explainable AI (XAI) has gained significant attention for providing insights into the decision-making processes of deep learning models, particularly for image classification tasks…
Visual Attention Prompted Prediction and Learning
Yifei Zhang, Siyi Gu, Bo Pan +4
Visual explanation (attention)-guided learning uses not only labels but also explanations to guide model reasoning process. While visual attention-guided learning has shown promisi…
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…
Knowledge-enhanced Neural Machine Reasoning: A Review
Tanmoy Chowdhury, Chen Ling, Xuchao Zhang +5
Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past f…
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…
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…