4 papers
Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
Xing Tang, Jingyang Bin, Ziqiang Cui +6
The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often strugg…
Test-Time Adaptation for Tactile-Vision-Language Models
Chuyang Ye, Haoxian Jing, Qinting Jiang +4
Tactile-vision-language (TVL) models are increasingly deployed in real-world robotic and multimodal perception tasks, where test-time distribution shifts are unavoidable. Existing…
Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation
Fuyuan Lyu, Zhentai Chen, Jingyan Jiang +4
Inspired by the success of language models (LM), scaling up deep learning recommendation systems (DLRS) has become a recent trend in the community. All previous methods tend to sca…
Beyond ADE and FDE: A Comprehensive Evaluation Framework for Safety-Critical Prediction in Multi-Agent Autonomous Driving Scenarios
Feifei Liu, Haozhe Wang, Zejun Wei +5
Current evaluation methods for autonomous driving prediction models rely heavily on simplistic metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE).…