15 citations · 37 across the 10 of their papers we have counts for
10 papers
JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators
Ce Chen, Congrui Wang, Yonglin Li +23
Text-to-image (T2I) diffusion models typically require substantial computational resources and cloud infrastructure, posing significant challenges for edge deployment in terms of l…
When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
Xinpeng Lv, Yunxin Mao, Renzhe Xu +13
Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are typically designed for \emph…
We Need a More Robust Classifier: Dual Causal Learning Empowers Domain-Incremental Time Series Classification
Zhipeng Liu, Peibo Duan, Xuan Tang +6
The World Wide Web thrives on intelligent services that rely on accurate time series classification, which has recently witnessed significant progress driven by advances in deep le…
UniSearch: Rethinking Search System with a Unified Generative Architecture
Jiahui Chen, Xiaoze Jiang, Zhibo Wang +18
Modern search systems play a crucial role in facilitating information acquisition. Traditional search engines typically rely on a cascaded architecture, where results are retrieved…
Mitigating Sensitive Information Leakage in LLMs4Code through Machine Unlearning
Shanzhi Gu, Zhaoyang Qu, Ruotong Geng +6
Large Language Models for Code (LLMs4Code) have achieved strong performance in code generation, but recent studies reveal that they may memorize and leak sensitive information cont…
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
Mingyang Geng, Shangwen Wang, Dezun Dong +5
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a…