7 papers
CoLLM-NAS: Collaborative Large Language Models for Efficient Knowledge-Guided Neural Architecture Search
Zhe Li, Zhiwei Lin, Yongtao Wang
The integration of Large Language Models (LLMs) with Neural Architecture Search (NAS) has introduced new possibilities for automating the design of neural architectures. However, m…
Variational autoencoder for inference of nonlinear mixed effect models based on ordinary differential equations
Zhe Li, Mélanie Prague, Rodolphe Thiébaut +1
We propose a variational autoencoder (VAE) approach for parameter estimation in nonlinear mixed-effects models based on ordinary differential equations (NLME-ODEs) using longitudin…
KRAL: Knowledge and Reasoning Augmented Learning for LLM-assisted Clinical Antimicrobial Therapy
Zhe Li, Yehan Qiu, Yujie Chen +1
Clinical antimicrobial therapy requires the dynamic integration of pathogen profiles,host factors, pharmacological properties of antimicrobials,and the severity of infection. This…
BLM: A Boundless Large Model for Cross-Space, Cross-Task, and Cross-Embodiment Learning
Wentao Tan, Bowen Wang, Heng Zhi +15
Multimodal large language models (MLLMs) have advanced vision-language reasoning and are increasingly deployed in embodied agents. However, significant limitations remain: MLLMs ge…
DL-QAT: Weight-Decomposed Low-Rank Quantization-Aware Training for Large Language Models
Wenjin Ke, Zhe Li, Dong Li +2
Improving the efficiency of inference in Large Language Models (LLMs) is a critical area of research. Post-training Quantization (PTQ) is a popular technique, but it often faces ch…
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models
Zheng Hu, Zhe Li, Ziyun Jiao +5
In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and int…