Results 21 to 30 of about 65,281 (258)

MCR-DL: Mix-and-Match Communication Runtime for Deep Learning

2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2023
In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and necessitated distribution among processors. Training such massive models necessitates advanced parallelism strategies to maintain efficiency.
Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar K. Panda 0001   +7 more
openaire   +2 more sources

Driver Drowsiness Detection using Evolutionary Machine Learning: A Survey [PDF]

BIO Web of Conferences
One of the factors that kills hundreds of people every year is driving accidents caused by drowsy drivers. There are different methods to prevent this type of accidents. Recently Machine Learning (ML) and Deep Learning (DL) have emerged as very effective
Yasir Jumhaa Maha, Majeed Osama, Taima Alaa   +2 more
doaj   +1 more source

Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

CAAI Transactions on Intelligence Technology, 2023
Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible.
Mehdi Gheisari, Fereshteh Ebrahimzadeh, Mohamadtaghi Rahimi, Mahdieh Moazzamigodarzi, Yang Liu, Pijush Kanti Dutta Pramanik, Mohammad Ali Heravi, Abolfazl Mehbodniya, Mustafa Ghaderzadeh, Mohammad Reza Feylizadeh, Saeed Kosari   +10 more
doaj   +1 more source

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships

Molecules, 2023
A deep learning-based quantitative structure–activity relationship analysis, namely the molecular image-based DeepSNAP–deep learning method, can successfully and automatically capture the spatial and temporal features in an image generated from a three ...
Yasunari Matsuzaka, Yoshihiro Uesawa
doaj   +1 more source

Deep Learning (DL)

, 2023
Deep learning (DL) is a highly impactful field in machine learning that has revolutionized various domains. DL can learn from large datasets, extract complex patterns, and make accurate predictions. It has applications in computer vision, natural language processing, healthcare, finance, and more.
openaire   +2 more sources

Deep learning methods may not outperform other machine learning methods on analyzing genomic studies

Frontiers in Genetics, 2022
Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small
Yao Dong, Yao Dong, Yao Dong, Shaoze Zhou, Li Xing, Yumeng Chen, Yumeng Chen, Ziyu Ren, Ziyu Ren, Yongfeng Dong, Yongfeng Dong, Xuekui Zhang   +11 more
doaj   +1 more source

A Novel Technique to Support Deep Learning Applications in a Model-Based Embedded Software Design Methodology

IEEE Access, 2023
As deep learning applications are getting popular in embedded systems, how to support deep learning applications in the model-based embedded software design methodology becomes a challenging problem. A previous solution is to represent each deep learning
Jangryul Kim, Jaewoo Son, Soonhoi Ha
doaj   +1 more source

A Comparative Study of Deep Learning and Traditional Methods for Environmental Remote Sensing [PDF]

ITM Web of Conferences, 2023
Because of the accessibility of massive data from remote sensing data and developments in ML, machine learning (ML) techniques have been extensively applied in environmental remote sensing research.
Farooq Bazila, Manocha Ankush
doaj   +1 more source

Deep Learning for Software Vulnerabilities Detection Using Code Metrics

IEEE Access, 2020
Software vulnerability can cause disastrous consequences for information security. Earlier detection of vulnerabilities minimizes these consequences.
Mohammed Zagane, Mustapha Kamel Abdi, Mamdouh Alenezi   +2 more
doaj   +1 more source

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning

Nature Communications, 2021
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) for brain imaging data analysis. Here, the authors show that if trained following prevalent DL practices, DL methods substantially improve compared ...
Anees Abrol, Zening Fu, Mustafa Salman, Rogers Silva, Yuhui Du, Sergey Plis, Vince Calhoun   +6 more
doaj   +1 more source