4 papers
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Lei Lu, Zhepeng Wang, Runxue Bao +7
Existing pruning techniques for large language models (LLMs) targeting domain-specific applications typically follow a two-stage process: pruning the pretrained general-purpose LLM…
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzin…
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Zhepeng Wang, Runxue Bao, Yawen Wu +6
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose s…
PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
Zhepeng Wang, Yi Sheng, Nirajan Koirala +4
Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a hi…