11 citations · 13 across the 9 of their papers we have counts for
5 papers · 1 filter
Astro: Activation-guided Structured Regularization for Outlier-Robust LLM Post-Training Quantization
Xi Chen, Ming Li, Junxi Li +5
Weight-only post-training quantization (PTQ) is crucial for efficient Large Language Model (LLM) deployment but suffers from accuracy degradation caused by weight and activation ou…
DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations
Yuhan Guo, Lizhong Ding, Shihan Jia +6
Explainable AI (XAI) builds trust in complex systems through model attribution methods that reveal the decision rationale. However, due to the absence of a unified optimal explanat…
DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model
Rongqing Li, Jiaqi Yu, Changsheng Li +3
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a…
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
Xi Chen, Kaituo Feng, Changsheng Li +4
Low-rank training has emerged as a promising approach for reducing memory usage in training Large Language Models (LLMs). Previous methods either rely on decomposing weight matrice…
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Kaituo Feng, Yikun Miao, Changsheng Li +2
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper te…