3 citations · 7 across the 7 of their papers we have counts for
4 papers · 1 filter
How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learning
Haotian Gao, Zheng Dong, Jiawei Yong +3
Spatio-temporal forecasting is essential for real-world applications such as traffic management and urban computing. Although recent methods have shown improved accuracy, they ofte…
Extracting Spatiotemporal Data from Gradients with Large Language Models
Lele Zheng, Yang Cao, Renhe Jiang +4
Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primar…
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
Toyotaro Suzumura, Akiyoshi Sugiki, Hiroyuki Takizawa +30
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability…
Automatic Graph Partitioning for Very Large-scale Deep Learning
Masahiro Tanaka, Kenjiro Taura, Toshihiro Hanawa +1
This work proposes RaNNC (Rapid Neural Network Connector) as middleware for automatic hybrid parallelism. In recent deep learning research, as exemplified by T5 and GPT-3, the size…