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Found 7 papers, 1 papers with code
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects.
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation.
Representation Learning with Information Theory for COVID-19 Detection
Successful data representation is a fundamental factor in machine learning based medical imaging analysis.
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
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 to detect walking people on the fly using retina-inspired, event-based cameras.
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs).
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
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 classification pipeline that employs variational autoencoders to extract efficient feature embedding.
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
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 specialized hardware.
