1 citations · 1 across the 5 of their papers we have counts for
5 papers
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Ying Nie, Kai Han, Hongguang Li +5
The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such a…
MoRAgent: Parameter Efficient Agent Tuning with Mixture-of-Roles
Jing Han, Binwei Yan, Tianyu Guo +4
Despite recent advancements of fine-tuning large language models (LLMs) to facilitate agent tasks, parameter-efficient fine-tuning (PEFT) methodologies for agent remain largely une…
Cross-video Identity Correlating for Person Re-identification Pre-training
Jialong Zuo, Ying Nie, Hanyu Zhou +5
Recent researches have proven that pre-training on large-scale person images extracted from internet videos is an effective way in learning better representations for person re-ide…
PanGu-: Enhancing Language Model Architectures via Nonlinearity Compensation
Yunhe Wang, Hanting Chen, Yehui Tang +17
The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which…
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity
Jialong Zuo, Hanyu Zhou, Ying Nie +5
Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts…