activity
20202025
most citedFrom Distributed Machine Learning to Federated Learning: A Survey

359 citations · 437 across the 17 of their papers we have counts for

collaborators
Showing 2024Show all

6 papers · 1 filter

q-bio.BM2024

Pre-trained Molecular Language Models with Random Functional Group Masking

Tianhao Peng, Yuchen Li, Xuhong Li +7

Recent advancements in computational chemistry have leveraged the power of trans-former-based language models, such as MoLFormer, pre-trained using a vast amount of simplified mole…

cs.AI2024

Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned

Yuan Liao, Jiang Bian, Yuhui Yun +9

While the field of NL2SQL has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving ful…

cs.AI2024★ 10 cited

Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents

Haoyi Xiong, Zhiyuan Wang, Xuhong Li +5

This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements. Traditionally considered dis…

cs.IR2024★ 2 cited

When Search Engine Services meet Large Language Models: Visions and Challenges

Haoyi Xiong, Jiang Bian, Yuchen Li +5

Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we sear…

eess.IV2024

Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training

Jiamin Chen, Xuhong Li, Yanwu Xu +2

Medical image segmentation aims to identify and locate abnormal structures in medical images, such as chest radiographs, using deep neural networks. These networks require a large…

cs.AI2024★ 8 cited

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective

Haoyi Xiong, Xuhong Li, Xiaofei Zhang +5

Given the complexity and lack of transparency in deep neural networks (DNNs), extensive efforts have been made to make these systems more interpretable or explain their behaviors i…