62 citations · 350 across the 33 of their papers we have counts for
6 papers · 1 filter
DeepFPC: Deep Unfolding of a Fixed-Point Continuation Algorithm for Sparse Signal Recovery from Quantized Measurements
Peng Xiao, Bin Liao, Nikos Deligiannis
We present DeepFPC, a novel deep neural network designed by unfolding the iterations of the fixed-point continuation algorithm with one-sided l1-norm (FPC-l1), which has been propo…
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Iman Marivani, Evaggelia Tsiligianni, Bruno Cornelis +1
Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge abo…
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
Evaggelia Tsiligianni, Nikos Deligiannis
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical o…
Rumour Detection via News Propagation Dynamics and User Representation Learning
Tien Huu Do, Xiao Luo, Duc Minh Nguyen +1
Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours; it thus…
Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm
Hung Duy Le, Huynh Van Luong, Nikos Deligiannis
We propose a new deep recurrent neural network (RNN) architecture for sequential signal reconstruction. Our network is designed by unfolding the iterations of the proximal gradient…
Geometric Matrix Completion with Deep Conditional Random Fields
Duc Minh Nguyen, Robert Calderbank, Nikos Deligiannis
The problem of completing high-dimensional matrices from a limited set of observations arises in many big data applications, especially, recommender systems. Existing matrix comple…