Results 31 to 40 of about 9,883,756 (332)
Image Segmentation Using Deep Learning: A Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and ...
Shervin Minaee +5 more
semanticscholar +1 more source
Revisiting Deep Learning Models for Tabular Data [PDF]
Neural Information Processing Systems, 2021 The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports competitive results on various datasets.
Yu. V. Gorishniy +3 more
semanticscholar +1 more source
Generative adversarial network-based attenuation correction for 99mTc-TRODAT-1 brain SPECT
Frontiers in Medicine, 2023 BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT).
Yu Du +9 more
doaj +1 more source
BMC Bioinformatics, 2021 Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions.
Pegah Mavaie +3 more
doaj +1 more source
IEEE Access, 2023 Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to ...
Haider Ali +7 more
doaj +1 more source
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Journal of Big Data, 2021 In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving ...
Laith Alzubaidi +9 more
semanticscholar +1 more source
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) - built, e.g., through proper orthogonal decomposition (POD) - when applied to nonlinear time-
S. Fresca, A. Manzoni
semanticscholar +1 more source
SN Computer Science, 2021 Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0).
I. H. Sarker
semanticscholar +1 more source
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction [PDF]
International Conference on Information and Knowledge Management, 2021 Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
Renhe Jiang +9 more
semanticscholar +1 more source
, 2023 This release is for the paper "DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications", and contains a ready-to-run demo code in Python Jupyter Notebook that uses the network trained on S ...
rizwanasif
core +1 more source