Search Results for author: Shiguang Wu

Found 7 papers, 2 papers with code

Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning

no code implementations27 Feb 2024 Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Jiahuan Pei

Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.

Instruction Following Natural Language Understanding

Learning Robust Sequential Recommenders through Confident Soft Labels

1 code implementation4 Nov 2023 Shiguang Wu, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Maarten de Rijke, Zhaochun Ren

CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.

Multi-class Classification Sequential Recommendation

Tree-Planner: Efficient Close-loop Task Planning with Large Language Models

no code implementations12 Oct 2023 Mengkang Hu, Yao Mu, Xinmiao Yu, Mingyu Ding, Shiguang Wu, Wenqi Shao, Qiguang Chen, Bin Wang, Yu Qiao, Ping Luo

This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations.

Decision Making

ColdNAS: Search to Modulate for User Cold-Start Recommendation

1 code implementation6 Jun 2023 Shiguang Wu, Yaqing Wang, Qinghe Jing, daxiang dong, Dejing Dou, Quanming Yao

Instead of using a fixed modulation function and deciding modulation position by expertise, we propose a modulation framework called ColdNAS for user cold-start problem, where we look for proper modulation structure, including function and position, via neural architecture search.

Neural Architecture Search Position +1

Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems

no code implementations12 Mar 2022 Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Shiguang Wu

Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities.

Graph Attention reinforcement-learning +1

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