16 citations · 42 across the 10 of their papers we have counts for
16 papers
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Chenhao Ye, Huaizheng Zhang, Mingcong Han +11
Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computational resources. However, existing…
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Mingjia Shi, Yuhao Zhou, Kai Wang +4
Classical federated learning (FL) enables training machine learning models without sharing data for privacy preservation, but heterogeneous data characteristic degrades the perform…
DataCI: A Platform for Data-Centric AI on Streaming Data
Huaizheng Zhang, Yizheng Huang, Yuanming Li
We introduce DataCI, a comprehensive open-source platform designed specifically for data-centric AI in dynamic streaming data settings. DataCI provides 1) an infrastructure with ri…
MIGPerf: A Comprehensive Benchmark for Deep Learning Training and Inference Workloads on Multi-Instance GPUs
Huaizheng Zhang, Yuanming Li, Wencong Xiao +7
New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This tech…
Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edges
Lei Zhang, Guanyu Gao, Huaizheng Zhang
Data drift is a thorny challenge when deploying person re-identification (ReID) models into real-world devices, where the data distribution is significantly different from that of…
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
Yizheng Huang, Huaizheng Zhang, Yuanming Li +2
The success of today's AI applications requires not only model training (Model-centric) but also data engineering (Data-centric). In data-centric AI, active learning (AL) plays a v…