Important Features Selection in Deep Neural Networks

Dear Colleagues,

The development of deep learning architectures continues to open up new fields of applications every day. However, several drawbacks of deep learning models are now well-known, including 1) the fact that the results are not easy to explain and 2) the large memory size needed for the models, which usually implies high computational costs for the results’ inference.

For this Special Issue, we invite submissions from researchers addressing feature selection within DNN architectures with techniques such as pruning or attention modules, with a focus on the data that are the most relevant for the ongoing task. We encourage authors to submit papers within different domains, such as NLP, computer vision, or multimedia frameworks. We also invite submissions from researchers studying DNN model compression to obtain lighter and faster models for edge computing while maintaining performance, in addition to researchers studying DNN attention models to better understand and explain the models’ decisions or to obtain more accurate results by focusing the models on the most interesting data for a given application.

This Special Issue aims to cover recent advances in DNN architecture compression, attention-based optimization, and understanding the model results. Reviews and surveys of the state-of-the-art DNN compression and attention models are also welcomed.

The topics of interest for this Special Issue include:

• DNN compression;
• DNN pruning;
• Attention-based modules;
• Transformers;
• Saliency-based explainability;
• Attention visualization;
• NLP and speech processing;
• Computer vision;
• Edge computing;
• Deployment and migration of AI applications on cloud/edge platforms;
• Explainable artificial intelligence, "XAI";
• Distributed and embedded deep learning.

However, please do not feel limited by these topics; we will consider submissions in any area of feature selection in DNN architectures. The Special Issue is linked to the TRAIL Institute for AI, Belgium, but is open to any submission.

Dr. Matei Mancas
Dr. Jean-Benoit Delbrouck
Dr. Sidi Ahmed Mahmoudi
Guest Editors

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