activity
20122025
most citedHET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework

57 citations · 168 across the 19 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

cs.DB2023

ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems

Jinqing Lian, Xinyi Zhang, Yingxia Shao +4

The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of oper…

cs.IR2023

Model-enhanced Vector Index

Hailin Zhang, Yujing Wang, Qi Chen +16

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in docume…

cs.LG2023

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

Tianmeng Yang, Min Zhou, Yujing Wang +4

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many res…

cs.DC2023

OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning

Youhe Jiang, Fangcheng Fu, Xupeng Miao +2

Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model re…

cs.DC202338 cited

FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

Xiaonan Nie, Xupeng Miao, Zilong Wang +5

With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these…

cs.DB20231 cited

A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

Xinyi Zhang, Zhuo Chang, Hong Wu +5

Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objec…