no code implementations • 27 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.
1 code implementation • 4 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.
no code implementations • 12 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.
no code implementations • 1 Oct 2023 • Shiguang Wu, Yaqing Wang, Quanming Yao
To address above problems, we propose a novel hierarchical adaptation mechanism for few-shot MPP (HiMPP).
1 code implementation • 6 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.
no code implementations • 17 Oct 2022 • Yiqun Chen, Hangyu Mao, Tianle Zhang, Shiguang Wu, Bin Zhang, Jianye Hao, Dong Li, Bin Wang, Hongxing Chang
Centralized Training with Decentralized Execution (CTDE) has been a very popular paradigm for multi-agent reinforcement learning.
no code implementations • 12 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.