3 citations · 3 across the 5 of their papers we have counts for
6 papers
Graph neural networks for sampling-invariant embeddings of organized signal sets
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo
Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes. Within suc…
Detecting radar targets swarms in range profiles with a partially complex-valued neural network
Martin Bauw
Correctly detecting radar targets is usually challenged by clutter and waveform distortion. An additional difficulty stems from the relative proximity of several targets, the latte…
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
Martin Bauw, Santiago Velasco-Forero, Jesus Angulo +2
Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OO…
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…