Call for papers - deep learning-based detection and recognition for perceptual tasks with applications

Deep learning has been popular in artificial intelligence with many applications due to great successes in many perceptual tasks (e.g., object detection, image understanding, and speech recognition). Moreover, deep learning is also critical in data science, especially for big data analytics relying on extracting high-level and complex abstractions as data representations based on a hierarchical learning process. In realizing deep learning, supervised and unsupervised approaches for training deep architectures have been empirically investigated based on the adoption of parallel computing facilities such as GPUs or CPU clusters. However, there is still limited understanding of why deep architectures work so well and how to design computationally efficient training algorithms and hardware acceleration techniques. At the same time, the number of end devices, such as IoT (Internet of Things) devices, has dramatically increased.