63 citations · 79 across the 11 of their papers we have counts for
6 papers · 1 filter
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Nina Miolane, Matteo Caorsi, Umberto Lupo +30
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Random projection is a common technique for designing algorithms in a variety of areas, including information retrieval, compressive sensing and measuring of outlyingness. In this…
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature. T…
Some open questions on morphological operators and representations in the deep learning era
Jesus Angulo
During recent years, the renaissance of neural networks as the major machine learning paradigm and more specifically, the confirmation that deep learning techniques provide state-o…
Scale Equivariant Neural Networks with Morphological Scale-Spaces
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant. Equivariance to other transformations (e.g. rotations, aff…
Going beyond p-convolutions to learn grayscale morphological operators
Alexandre Kirszenberg, Guillaume Tochon, Elodie Puybareau +1
Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with eros…