1 citations · 1 across the 5 of their papers we have counts for
5 papers
TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
Xiangyu Chen, Jing Liu, Ye Wang +4
To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine-tuning the model…
LatentLLM: Attention-Aware Joint Tensor Compression
Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu +4
Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new f…
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
Jian Xu, Sichun Luo, Xiangyu Chen +3
Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further…
SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer Attention Modules
Xiangyu Chen, Jing Liu, Ye Wang +4
Low-rank adaptation (LoRA) and its variants are widely employed in fine-tuning large models, including large language models for natural language processing and diffusion models fo…
MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices
Xiangyu Chen, Ruiwen Zhen, Shuai Li +2
Image restoration aims to restore high-quality images from degraded counterparts and has seen significant advancements through deep learning techniques. The technique has been wide…