7 citations · 19 across the 6 of their papers we have counts for
6 papers
Learning to SLAM on the Fly in Unknown Environments: A Continual Learning Approach for Drones in Visually Ambiguous Scenes
Ali Safa, Tim Verbelen, Ilja Ocket +4
Learning to safely navigate in unknown environments is an important task for autonomous drones used in surveillance and rescue operations. In recent years, a number of learning-bas…
Representation Learning with Information Theory for COVID-19 Detection
Abel Díaz Berenguer, Tanmoy Mukherjee, Matias Bossa +2
Successful data representation is a fundamental factor in machine learning based medical imaging analysis. Deep Learning (DL) has taken an essential role in robust representation l…
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
Ali Safa, Ilja Ocket, André Bourdoux +3
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn…
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
Ali Safa, Ilja Ocket, André Bourdoux +3
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs). In contrast to empirical parameter sear…
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised c…
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
Meshia Cédric Oveneke, Mitchel Aliosha-Perez, Yong Zhao +2
The omnipresence of deep learning architectures such as deep convolutional neural networks (CNN)s is fueled by the synergistic combination of ever-increasing labeled datasets and s…