The next JSAIT special issue will be on deep learning methods for inverse problems. Papers are due May 15, 2022.  Read the call for papers. 

Deep learning methods have emerged as highly successful tools for solving inverse problems. They achieve state-of-the-art performance on tasks such as image denoising, inpainting, super-resolution, and compressive sensing. They are also starting to be used in inverse problems beyond imaging, including for solving inverse problems arising in communications, signal processing, and even on non-Euclidean data such as graphs. However, a wide range of important theoretical and practical questions remain unsolved or even completely open, including precise theoretical guarantees for signal recovery, robustness and out-of-distribution generalization, architecture design, and domain-specific applications and challenges. This special issue aims to advance cutting-edge research in this area, with an emphasis on its intersection with information theory