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Deep learning (DL) has been wildly successful in practice, and most of the state-of-the-art machine learning methods are based on neural networks (NNs). Lacking, however, is a rigorous mathematical theory that adequately explains the amazing performance of deep NNs (DNNs). In this article, we present a relatively new mathematical framework that provides the beginning of a deeper understanding of DL. This framework precisely characterizes the functional properties of NNs that are trained to fit to data. The key mathematical tools that support this framework include transform-domain sparse regularization, the Radon transform of computed tomography, and approximation theory, which are all techniques deeply rooted in signal processing.
Compression is essential for efficient storage and transmission of signals. One powerful method for compression is through the application of orthogonal transforms, which convert a group of
As we gear up for the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2024, it is essential to take a moment to celebrate the achievements and highlights of ICASSP 2023, which took place on Rhodes Island, Greece, this past June. ICASSP 2023 was a momentous event as it marked the first postpandemic ICASSP, and the return to in-person meetings. With the theme “Signal Processing in the AI Era,” the conference underscored the strong connection between signal processing and machine learning, highlighting the pivotal role of signal processing in shaping the development of artificial intelligence (AI).
The objectives of IEEE Signal Processing Magazine ( SPM ) are to propose, for any IEEE Signal Processing Society (SPS) member and beyond, a wide range of tutorial articles on both methods and applications in signal and image processing. The articles are divided into different categories: feature articles, column and forum articles, and articles in special issues, the specificities of which are detailed on the SPM webpage “Information for Authors - SPM”.
Date: 17 September 2023
Chapter: Uttar Pradesh Chapter
Chapter Chair: Satish Kumar Singh
Title: Personal Information Devices: Portable to wearable, Stand-alone to connected, players to sensors
Date: 20 October 2023
Chapter: Delhi Chapter
Chapter Chair: Monika Aggarwal
Title: AI and Machine Learning Applications
Date: 17 October 2023
Chapter: Gujarat Chapter
Chapter Chair: Chirag N. Paunwala
Title: AI Techniques for Healthcare
Date: 14-15 September 2023
Chapter: Madras Chapter
Chapter Chair: Dr. N. Venkateswaran
Title: Easy and Lazy Technical Writing : How to build a solid logic