Zobrazeno 1 - 10
of 492
pro vyhledávání: '"Distributed deep learning"'
Publikováno v:
IEEE Access, Vol 12, Pp 116891-116904 (2024)
Spot Virtual Machines (Spot VMs) offer access to underutilized computing resources at significant discounts, sometimes up to 90% off regular on-demand pricing. For budget-conscious organizations, using clusters of Spot VMs is an effective strategy fo
Externí odkaz:
https://doaj.org/article/370e5d65483447189aec74b4ec22de47
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-37 (2024)
Abstract Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a comprehensive workflow for deploying and optimizing the YOLACT instance segmentation model as on big d
Externí odkaz:
https://doaj.org/article/bccd7cee86bc432d8e3a82d840ab1df3
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 129, Iss , Pp 103840- (2024)
With the introduction of deep learning methods, the computation required for remote sensing change detection has significantly increased, and distributed computing is applied to remote sensing change detection to improve computational efficiency. How
Externí odkaz:
https://doaj.org/article/9e0c10375eda4e0f83ac81685ab472e0
Autor:
Mohammad Dehghani, Zahra Yazdanparast
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-21 (2023)
Abstract Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning algorithms. However, to acquire more accurate outcomes and solve more complex issues, algorithms
Externí odkaz:
https://doaj.org/article/7f64f949ef584912b7ee42fe04d44919
Autor:
Sookwang Lee, Jaehwan Lee
Publikováno v:
Applied Sciences, Vol 14, Iss 12, p 5100 (2024)
In distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time. Representative collective communication libraries such as MPI, GLOO, and NC
Externí odkaz:
https://doaj.org/article/0ae28116525a4f4f8f9e572579e497e3
Autor:
Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim, Hyunhee Cho, Jihong Park, Minseok Choi, Soyi Jung, Joongheon Kim
Publikováno v:
ICT Express, Vol 9, Iss 3, Pp 486-491 (2023)
Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this pro
Externí odkaz:
https://doaj.org/article/636e228595e04231b713f26c6b87b245
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-23 (2023)
Abstract Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an analysis within a realistic timeframe. Deep learning models have proven to be capable of underst
Externí odkaz:
https://doaj.org/article/1ea7daa2801f435cb665bebd532759dc
Autor:
Aswathy Ravikumar, Harini Sriraman
Publikováno v:
Heliyon, Vol 10, Iss 1, Pp e23567- (2024)
The recent advancement in deep learning with growth in big data and high-performance computing is Distributed Deep Learning. The rapid rise in the volume of data and network complexity has led to significant growth in DDL. Distribution of the network
Externí odkaz:
https://doaj.org/article/dafa7d39619d4f1ba641eff444891b1d
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2361 (2024)
Litopenaeus vannamei is a common species in aquaculture and has a high economic value. However, Litopenaeus vannamei are often invaded by pathogenic bacteria and die during the breeding process, so it is of great significance to study the identificat
Externí odkaz:
https://doaj.org/article/2049b563fa1946be96721e9bf050568a
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