[HTML][HTML] Data augmentation: A comprehensive survey of modern approaches
A Mumuni, F Mumuni - Array, 2022 - Elsevier
To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
[HTML][HTML] Recurrent neural networks: A comprehensive review of architectures, variants, and applications
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …
(ML) by enabling the effective processing of sequential data. This paper provides a …
R-drop: Regularized dropout for neural networks
Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …
networks. Though effective and performing well, the randomness introduced by dropout …
AI applications to medical images: From machine learning to deep learning
Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …
research and healthcare services. This review focuses on challenges points to be clarified …
Avoiding overfitting: A survey on regularization methods for convolutional neural networks
Several image processing tasks, such as image classification and object detection, have
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …
COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis
Social media (and the world at large) have been awash with news of the COVID-19
pandemic. With the passage of time, news and awareness about COVID-19 spread like the …
pandemic. With the passage of time, news and awareness about COVID-19 spread like the …
A comprehensive survey on regularization strategies in machine learning
Y Tian, Y Zhang - Information Fusion, 2022 - Elsevier
In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …
ability means that the model not only performs well on the training data set, but also can …
A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …
Avoiding a replication crisis in deep-learning-based bioimage analysis
Deep learning algorithms are powerful tools for analyzing, restoring and transforming
bioimaging data. One promise of deep learning is parameter-free one-click image analysis …
bioimaging data. One promise of deep learning is parameter-free one-click image analysis …
A review of image super-resolution approaches based on deep learning and applications in remote sensing
At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …
images are becoming more widely used in real scenes. However, due to the limitations of …