Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

Deep Neural Networks (DNNs) are being adopted as components in software systems.
Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the …

Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in
cognitive, perceptual, social, emotional, and behavioral functions. In the traditional …

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 …

Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks
and platforms play a key role to catalyze such progress. However, the differences in …

Deep learning (DL) systems are widely used in domains including aircraft collision
avoidance systems, Alzheimer's disease diagnosis, and autonomous driving cars. Despite …

Background Cell nuclei segmentation is a fundamental task in microscopy image analysis,
based on which multiple biological related analysis can be performed. Although deep …

Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial,
especially for safety-critical applications. Existing work mainly focuses on the quality …

Deep learning has gained tremendous success in various fields while training deep neural
networks (DNNs) is very compute-intensive, which results in numerous deep learning …

Big data powered Deep Learning (DL) and its applications have blossomed in recent years,
fueled by three technological trends: a large amount of digitized data openly accessible, a …