Results 61 to 70 of about 120,084 (296)

Towards a Resource Efficient Framework for Distributed Deep Learning Applications

, 2022
Distributed deep learning has achieved tremendous success for solving scientific problems in research and discovery over the past years. Deep learning training is quite challenging because it requires training on large-scale massive dataset, especially ...
Han, Jingoo
core  

A Distributed Deep Learning Network Based on Data Enhancement for Few-Shot Raman Spectral Classification of Litopenaeus vannamei Pathogens

Applied Sciences
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 identification ...
Yanan Chen, Zheng Li, Ming Chen
doaj   +1 more source

Distributed Multi-Intersection Traffic Flow Prediction using Deep Learning [PDF]

E3S Web of Conferences
Efficient traffic flow prediction is paramount in modern urban transportation management, contributing significantly to energy efficiency and overall sustainability.
Moumen Idriss, Mahdaoui Rabie, Raji Fatima Zahra, Rafalia Najat, Abouchabaka Jaafar   +4 more
doaj   +1 more source

Instance segmentation on distributed deep learning big data cluster

Journal of Big Data
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 data clusters.
Mohammed Elhmadany, Islam Elmadah, Hossam E. Abdelmunim   +2 more
doaj   +1 more source

Modelling stem cell differentiation related processes—A practical overview for biologists

FEBS Letters, EarlyView.
Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar, Xinqi Yan, Lucas Jansen Klomp, Stefano Schivo, Janine N. Post   +4 more
wiley   +1 more source

Distributed Deep Learning Techniques for Remote Sensing Applications

, 2023
Distributed Deep Learning is a rapidly growing field that is concerned with training deep neural networks on multiple GPUs or even across multiple nodes.
Kumari, Garima
core   +1 more source

AFSD: Adaptive Feature Space Distillation for Distributed Deep Learning

IEEE Access, 2022
We propose a novel and adaptive feature space distillation method (AFSD) to reduce the communication overhead among distributed computers. The proposed method improves the Codistillation process by supporting longer update interval rates.
Salman Khaleghian, Habib Ullah, Einar Broch Johnsen, Anders Andersen, Andrea Marinoni   +4 more
doaj   +1 more source

Adversarial Distributional Training for Robust Deep Learning

CoRR, 2020
NeurIPS 2020.
Zhijie Deng, Yinpeng Dong, Tianyu Pang, Hang Su 0006, Jun Zhu 0001   +4 more
openaire   +3 more sources

Out-of-Distribution Robustness in Deep Learning Compression

CoRR, 2021
Initially published at ICML-2021 ITR3 ...
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti   +2 more
openaire   +2 more sources

Design and analysis strategies for robust microbiome ageing research

FEBS Letters, EarlyView.
The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik, İsmail Güderer, Yi Wang, Tayyaba Alvi, Prasoon Pandey, Handan Melike Dönertaş   +5 more
wiley   +1 more source