Submission Deadline: 31 December 2019
IEEE Access invites manuscript submissions in the area of Scalable Deep Learning for Big Data.
Artificial Intelligence (AI), and specifically Deep Learning (DL), are trending to become integral components of every service in our future digital society and economy. This is mainly due to the rise in computational power and advances in data science. DL’s inherent ability to discover correlations from vast quantities of data in an unsupervised fashion has been the main drive for its wide adoption.
Deep Learning also enables dynamic discovery of features from data, unlike traditional machine learning approaches, where feature selection remains a challenge. Deep Learning has been applied to various domains such as speech recognition and image classification, nature language processing, and computer vision.
Typical deep neural networks (DNN) require large amounts of data to learn parameters (often reaching to millions), which is a computationally intensive process requiring significant time to train a model. As the data size increases exponentially and the deep learning models become more complex, it requires more computing power and memory, such as high performance computing (HPC) resources to train an accuracy model in a timely manner. Despite existing efforts in training and inference of deep learning models to increase concurrency, many existing training algorithms for deep learning are notoriously difficult to scale and parallelize due to inherent interdependencies within the computation steps as well as the training data. The existing methods are not sufficient to systematically harness such systems/clusters.
Therefore, there is a need to develop new parallel and distributed algorithms/frameworks for scalable deep learning which can speed up the training process and make it suitable for big data processing and analysis.
This Special Section in IEEE Access aims to solicit research contributions from academia and industry which addresses key challenges in big data processing and analysis using scalable deep learning.
The topics of interest include, but are not limited to:
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Associate Editor: Liangxiu Han, Manchester Metropolitan University, UK
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Prof. Derek Abbott, University of Adelaide
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