M Chen, Z Wei, Z Huang, B Ding… - … conference on machine …, 2020 - proceedings.mlr.press
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph- structured data. Recently, GCNs and subsequent variants have shown superior performance …
Modern practice for training classification deepnets involves a terminal phase of training (TPT), which begins at the epoch where training error first vanishes. During TPT, the training …
V Monga, Y Li, YC Eldar - IEEE Signal Processing Magazine, 2021 - ieeexplore.ieee.org
Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and …
Learning to optimize (L2O) is an emerging approach that leverages machine learning to develop optimization methods, aiming at reducing the laborious iterations of hand …
D Ulyanov, A Vedaldi… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
We describe the new field of the mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of …
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic …
Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and …
Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …