3 papers
cs.CV2020
Multi-view adaptive graph convolutions for graph classification
Nikolas Adaloglou, Nicholas Vretos, Petros Daras
In this paper, a novel multi-view methodology for graph-based neural networks is proposed. A systematic and methodological adaptation of the key concepts of classical deep learning…
eess.SP2019
An Improved Tobit Kalman Filter with Adaptive Censoring Limits
Kostas Loumponias, Nicholas Vretos, George Tsaklidis +1
This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements whi…
cs.CV2017
Non-linear Convolution Filters for CNN-based Learning
Georgios Zoumpourlis, Alexandros Doumanoglou, Nicholas Vretos +1
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration b…