40 citations · 69 across the 12 of their papers we have counts for
5 papers · 1 filter
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Bo Pan, Zhen Xiong, Guanchen Wu +3
Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…
Explaining latent representations of generative models with large multimodal models
Mengdan Zhu, Zhenke Liu, Bo Pan +2
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence. With the rise of the large multimodal…
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…
Controllable Data Generation Via Iterative Data-Property Mutual Mappings
Bo Pan, Muran Qin, Shiyu Wang +2
Deep generative models have been widely used for their ability to generate realistic data samples in various areas, such as images, molecules, text, and speech. One major goal of d…
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…