568 citations · 874 across the 25 of their papers we have counts for
7 papers · 1 filter
Accelerating Gossip SGD with Periodic Global Averaging
Yiming Chen, Kun Yuan, Yingya Zhang +3
Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient th…
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
Kun Yuan, Yiming Chen, Xinmeng Huang +4
The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is…
Practical Relative Order Attack in Deep Ranking
Mo Zhou, Le Wang, Zhenxing Niu +4
Recent studies unveil the vulnerabilities of deep ranking models, where an imperceptible perturbation can trigger dramatic changes in the ranking result. While previous attempts fo…
Large-Scale Training System for 100-Million Classification at Alibaba
Liuyihan Song, Pan Pan, Kang Zhao +5
In the last decades, extreme classification has become an essential topic for deep learning. It has achieved great success in many areas, especially in computer vision and natural…
Balanced Order Batching with Task-Oriented Graph Clustering
Lu Duan, Haoyuan Hu, Zili Wu +4
Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking pro…
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
Lu Duan, Haoyuan Hu, Yu Qian +4
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer's order usually contains several items and needs to be pa…
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
Yujing Hu, Qing Da, Anxiang Zeng +2
In e-commerce platforms such as Amazon and TaoBao, ranking items in a search session is a typical multi-step decision-making problem. Learning to rank (LTR) methods have been widel…