8 citations · 18 across the 15 of their papers we have counts for
6 papers · 1 filter
An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning
Shaohuai Shi, Qing Yang, Yang Xiang +2
To enable the pre-trained models to be fine-tuned with local data on edge devices without sharing data with the cloud, we design an efficient split fine-tuning (SFT) framework for…
Listening to Users' Voice: Automatic Summarization of Helpful App Reviews
Cuiyun Gao, Yaoxian Li, Shuhan Qi +4
App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features…
Dynamic Contrastive Distillation for Image-Text Retrieval
Jun Rao, Liang Ding, Shuhan Qi +4
Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major d…
Parameter-Efficient and Student-Friendly Knowledge Distillation
Jun Rao, Xv Meng, Liang Ding +2
Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixe…
Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning
Shuhan Qi, Shuhao Zhang, Xiaohan Hou +3
Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor s…
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
Jun Rao, Fei Wang, Liang Ding +4
This article aims to provide the information retrieval community with some reflections on recent advances in retrieval learning by analyzing the reproducibility of image-text retri…