activity
20182025
most citedFSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

12 citations · 21 across the 10 of their papers we have counts for

collaborators

10 papers

cs.DC2025

DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization

Zhenheng Tang, Zichen Tang, Junlin Huang +7

The growth of large language models (LLMs) increases challenges of accelerating distributed training across multiple GPUs in different data centers. Moreover, concerns about data p…

cs.CV2025

SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion

Qingsong Yan, Qiang Wang, Kaiyong Zhao +4

Due to the rapid development of panorama cameras, the task of estimating panorama depth has attracted significant attention from the computer vision community, especially in applic…

cs.LG2025★ 12 cited

FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models

Xinglin Pan, Wenxiang Lin, Lin Zhang +5

Recent large language models (LLMs) have tended to leverage sparsity to reduce computations, employing the sparsely activated mixture-of-experts (MoE) technique. MoE introduces fou…

cs.DC2024★ 2 cited

FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression

Zhenheng Tang, Xueze Kang, Yiming Yin +11

To alleviate hardware scarcity in training large deep neural networks (DNNs), particularly large language models (LLMs), we present FusionLLM, a decentralized training system desig…

cs.DC2024

Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules

Xinglin Pan, Wenxiang Lin, Shaohuai Shi +3

Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in…

cs.CV2023

CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning

Qingsong Yan, Qiang Wang, Kaiyong Zhao +4

Neural Radiance Fields (NeRF) have demonstrated impressive performance in novel view synthesis. However, NeRF and most of its variants still rely on traditional complex pipelines t…

cs.CV2022★ 2 cited

Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity

Qingsong Yan, Qiang Wang, Kaiyong Zhao +3

Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this…

cs.CV2022

SphereDepth: Panorama Depth Estimation from Spherical Domain

Qingsong Yan, Qiang Wang, Kaiyong Zhao +3

The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc. However, the…

cs.DC2020★ 1 cited

A Quantitative Survey of Communication Optimizations in Distributed Deep Learning

Shaohuai Shi, Zhenheng Tang, Xiaowen Chu +3

Nowadays, large and complex deep learning (DL) models are increasingly trained in a distributed manner across multiple worker machines, in which extensive communications between wo…

cs.DC2018★ 4 cited

MG-WFBP: Efficient Data Communication for Distributed Synchronous SGD Algorithms

Shaohuai Shi, Xiaowen Chu, Bo Li

Distributed synchronous stochastic gradient descent has been widely used to train deep neural networks on computer clusters. With the increase of computational power, network commu…