1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
RED: Effective Trajectory Representation Learning with Comprehensive Information
Silin Zhou, Shuo Shang, Lisi Chen +2
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory cl…
Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Silin Zhou, Shuo Shang, Lisi Chen +2
Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks. Existing TRL methods use either grid trajectories, capturing movem…
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Shen Gao, Yifan Wang, Jiabao Fang +3
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior.However, the lack of transparency in presenting recommendations can lead to u…
360REA: Towards A Reusable Experience Accumulation with 360° Assessment for Multi-Agent System
Shen Gao, Hao Li, Chengrui Huang +4
Large language model agents have demonstrated remarkable advancements across various complex tasks. Recent works focus on optimizing the agent team or employing self-reflection to…
Not All Layers of LLMs Are Necessary During Inference
Siqi Fan, Xin Jiang, Xiang Li +6
Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to h…