Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR)
algorithms, possibly tackling financial and manpower challenges hindering implementation …
algorithms, possibly tackling financial and manpower challenges hindering implementation …
Demystifying learning rate policies for high accuracy training of deep neural networks
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep
neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivial to …
neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivial to …
Machine learning for security and the internet of things: the good, the bad, and the ugly
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …
collection, automation, and remote sensing and actuation, transforming autonomous …
Microscopy cell nuclei segmentation with enhanced U-Net
F Long - BMC bioinformatics, 2020 - Springer
Background Cell nuclei segmentation is a fundamental task in microscopy image analysis,
based on which multiple biological related analysis can be performed. Although deep …
based on which multiple biological related analysis can be performed. Although deep …
DLBench: a comprehensive experimental evaluation of deep learning frameworks
Deep Learning (DL) has achieved remarkable progress over the last decade on various
tasks such as image recognition, speech recognition, and natural language processing. In …
tasks such as image recognition, speech recognition, and natural language processing. In …
An overview of the data-loader landscape: Comparative performance analysis
Dataloaders, in charge of moving data from storage into GPUs while training machine
learning models, might hold the key to drastically improving the performance of training jobs …
learning models, might hold the key to drastically improving the performance of training jobs …
Document-level multi-topic sentiment classification of email data with bilstm and data augmentation
Email data has unique characteristics, involving multiple topics, lengthy replies, formal
language, high variance in length, high duplication, anomalies, and indirect relationships …
language, high variance in length, high duplication, anomalies, and indirect relationships …
Selecting and composing learning rate policies for deep neural networks
The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to
the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training …
the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the training …
What is the intended usage context of this model? An exploratory study of pre-trained models on various model repositories
There is a trend of researchers and practitioners to directly apply pre-trained models to solve
their specific tasks. For example, researchers in software engineering (SE) have …
their specific tasks. For example, researchers in software engineering (SE) have …
Rethinking learning rate tuning in the era of large language models
Large Language Models (LLMs) represent the recent success of deep learning in achieving
remarkable human-like predictive performance. It has become a mainstream strategy to …
remarkable human-like predictive performance. It has become a mainstream strategy to …