12 citations · 12 across the 5 of their papers we have counts for
5 papers
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Youshao Xiao, Lin Ju, Zhenglei Zhou +8
Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large data and rich features. However, stragglers…
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang +5
With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical…
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
Youshao Xiao, Shangchun Zhao, Zhenglei Zhou +5
Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in col…
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
Chan Wu, Hanxiao Zhang, Lin Ju +8
Recently, various distributed strategies for large language model training have been proposed. However, these methods provided limited solutions for the trade-off between memory co…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…