Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism

4 November 2024

Papers citing "Real-time and Downtime-tolerant Fault Diagnosis for Railway Turnout Machines (RTMs) Empowered with Cloud-Edge Pipeline Parallelism"

5 / 5 papers shown

Title
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models Zhiquan Lai Shengwei Li Xudong Tang Ke-shi Ge Weijie Liu Yabo Duan Linbo Qiao Dongsheng Li 83 44 0 10 Jun 2022
Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs John Thorpe Pengzhan Zhao Jon Eyolfson Yifan Qiao Zhihao Jia Minjia Zhang Ravi Netravali Guoqing Harry Xu 65 58 0 26 Apr 2022
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals Yu-xin Zhang Yiqiang Chen Jindong Wang Zhiwen Pan AI4TS 92 195 0 27 Jul 2021
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism Yosuke Oyama N. Maruyama Nikoli Dryden Erin McCarthy P. Harrington J. Balewski Satoshi Matsuoka Peter Nugent B. Van Essen 3DV AI4CE 68 37 0 25 Jul 2020
A review on outlier/anomaly detection in time series data Ane Blázquez-García Angel Conde U. Mori Jose A. Lozano AI4TS 69 743 0 11 Feb 2020