6 papers
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Yu Mao, Jingzong Li, Jun Wang +4
Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between di…
Lossless Compression of Large Language Model-Generated Text via Next-Token Prediction
Yu Mao, Holger Pirk, Chun Jason Xue
As large language models (LLMs) continue to be deployed and utilized across domains, the volume of LLM-generated data is growing rapidly. This trend highlights the increasing impor…
WISE: A Framework for Gigapixel Whole-Slide-Image Lossless Compression
Yu Mao, Jun Wang, Nan Guan +1
Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides,…
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models
Weilan Wang, Yu Mao, Dongdong Tang +3
Large language models (LLMs) exhibit excellent performance in various tasks. However, the memory requirements of LLMs present a great challenge when deploying on memory-limited dev…
BAHOP: Similarity-based Basin Hopping for A fast hyper-parameter search in WSI classification
Jun Wang, Yu Mao, Yufei Cui +2
Pre-processing whole slide images (WSIs) can impact classification performance. Our study shows that using fixed hyper-parameters for pre-processing out-of-domain WSIs can signific…
SHAP-CAT: A interpretable multi-modal framework enhancing WSI classification via virtual staining and shapley-value-based multimodal fusion
Jun Wang, Yu Mao, Nan Guan +1
The multimodal model has demonstrated promise in histopathology. However, most multimodal models are based on H\&E and genomics, adopting increasingly complex yet black-box designs…