23 citations · 63 across the 23 of their papers we have counts for
7 papers · 1 filter
SeSeMI: Secure Serverless Model Inference on Sensitive Data
Guoyu Hu, Yuncheng Wu, Gang Chen +2
Model inference systems are essential for implementing end-to-end data analytics pipelines that deliver the benefits of machine learning models to users. Existing cloud-based model…
NeurDB: On the Design and Implementation of an AI-powered Autonomous Database
Zhanhao Zhao, Shaofeng Cai, Haotian Gao +8
Databases are increasingly embracing AI to provide autonomous system optimization and intelligent in-database analytics, aiming to relieve end-user burdens across various industry…
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
Fei Xiao, Shaofeng Cai, Gang Chen +3
Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-ba…
NeurDB: An AI-powered Autonomous Data System
Beng Chin Ooi, Shaofeng Cai, Gang Chen +9
In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises…
Powering In-Database Dynamic Model Slicing for Structured Data Analytics
Lingze Zeng, Naili Xing, Shaofeng Cai +4
Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract s…
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Jiaqi Zhu, Shaofeng Cai, Fang Deng +2
Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language. Recent explorations have utilized LVLMs to tackle zero-s…
pTNAS: Progressive Neural Architecture Search for Tabular Data
Naili Xing, Shaofeng Cai, Lingze Zeng +4
Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context…