Results 21 to 30 of about 120,084 (296)
International Journal of Applied Earth Observations and Geoinformation, 2022 Distributed scatter (DS) interferometric synthetic aperture radar is a powerful technology for analyzing displacements of the earth's surface. Unfortunately, the preparatory step of DS pre-processing is enormously time consuming.
Duo Wang +2 more
doaj +1 more source
Analysis of Distributed Deep Learning in the Cloud
CoRR, 2022 We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud. We have implemented the profiler by extending prior work to additionally estimate two types of communication stalls - interconnect and network ...
Aakash Sharma +8 more
openaire +2 more sources
IEEE Access, 2020 Distributed processing using high-performance computing resources is essential for developers to train large-scale deep neural networks (DNNs). The major impediment to distributed DNN training is the communication bottleneck during the parameter exchange
Shinyoung Ahn, Eunji Lim
doaj +1 more source
Federated Learning: A Distributed Shared Machine Learning Method
Complexity, 2021 Federated learning (FL) is a distributed machine learning (ML) framework. In FL, multiple clients collaborate to solve traditional distributed ML problems under the coordination of the central server without sharing their local private data with others ...
Kai Hu +6 more
doaj +1 more source
Distributed Deep Learning for Precipitation Nowcasting [PDF]
2019 IEEE High Performance Extreme Computing Conference (HPEC), 2019 IEEE HPEC ...
Siddharth Samsi +2 more
openaire +2 more sources
Hierarchical Distribution-aware Testing of Deep Learning
ACM Transactions on Software Engineering and Methodology, 2023 With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Input added with adversarial perturbations, i.e., Adversarial Examples (AEs) , are easily mispredicted by the ...
Wei Huang 0035 +4 more
openaire +2 more sources
Elephas: Distributed Deep Learning with Keras & Spark
, 2022 Elephas is an extension of Keras, which allows you to run distributed deep learning models at scale with Apache ...
Cahall, Daniel, Pumperla, Max
core +1 more source
Distributed Deep Learning for Question Answering [PDF]
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016 This paper will appear in the Proceeding of The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis ...
Minwei Feng, Bing Xiang, Bowen Zhou 0006
openaire +2 more sources
CoRR, 2015 Updated figure of distributed deep learning architecture, updated content throughout paper including dealing with minor grammatical issues and highlighting differences of our paper with respect to prior work.
Hao Yi Ong, Kevin Chavez, Augustus Hong
openaire +2 more sources
Distributed Deep Reinforcement Learning: An Overview
CoRR, 2020 15 pages, 9 ...
Mohammad Reza Samsami, Hossein Alimadad
openaire +2 more sources