Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Prospective deployment of deep learning in MRI: a framework for important considerations, challenges, and recommendations for best practices
Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved Transformer
Y Hou, J Wang, Z Chen, J Ma, T Li - Engineering Applications of Artificial …, 2023 - Elsevier
Aiming at the problems of low accuracy and robustness of traditional deep learning fault
diagnosis methods, a novel attention-based multi-feature parallel fusion model …
diagnosis methods, a novel attention-based multi-feature parallel fusion model …
Accelerating training of transformer-based language models with progressive layer dropping
Recently, Transformer-based language models have demonstrated remarkable
performance across many NLP domains. However, the unsupervised pre-training step of …
performance across many NLP domains. However, the unsupervised pre-training step of …
Improving deep transformer with depth-scaled initialization and merged attention
The general trend in NLP is towards increasing model capacity and performance via deeper
neural networks. However, simply stacking more layers of the popular Transformer …
neural networks. However, simply stacking more layers of the popular Transformer …
ResNet autoencoders for unsupervised feature learning from high-dimensional data: Deep models resistant to performance degradation
Efficient modeling of high-dimensional data requires extracting only relevant dimensions
through feature learning. Unsupervised feature learning has gained tremendous attention …
through feature learning. Unsupervised feature learning has gained tremendous attention …
Two-stage channel estimation for mmWave massive MIMO systems based on ResNet-UNet
J Zhao, Y Wu, Q Zhang, J Liao - IEEE Systems Journal, 2023 - ieeexplore.ieee.org
For millimeter wave massive multiple-input multiple-output systems, the transceiver usually
adopts a hybrid precoding structure to reduce complexity and cost, which poses great …
adopts a hybrid precoding structure to reduce complexity and cost, which poses great …
A transfer learning approach for damage diagnosis in composite laminated plate using Lamb waves
Lamb wave-based damage diagnosis systems are widely regarded as a likely candidate for
real-time structural health monitoring (SHM), although analysing the Lamb wave response is …
real-time structural health monitoring (SHM), although analysing the Lamb wave response is …
Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets
M Chetoui, MA Akhloufi - Journal of Medical Imaging, 2020 - spiedigitallibrary.org
Purpose: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people
having diabetes for several years. It is one of the leading causes of visual impairment …
having diabetes for several years. It is one of the leading causes of visual impairment …
Improved Residual Network based on norm-preservation for visual recognition
Abstract Residual Network (ResNet) achieves deeper and wider networks with high-
performance gains, representing a powerful convolutional neural network architecture. In …
performance gains, representing a powerful convolutional neural network architecture. In …