758 citations · 906 across the 7 of their papers we have counts for
8 papers
Designing a Pseudo-Random Bit Generator with a Novel 5D-Hyperchaotic System
Ngoc T. Nguyen, Toan Q. Bui, Ghyslain Gagnon +2
Dynamic and non-linear systems are emerging as potential candidates for random bit generation. In this context, chaotic systems, which are both dynamic and stochastic, are particul…
Energy Disaggregation using Variational Autoencoders
Antoine Langevin, Marc-André Carbonneau, Mohamed Cheriet +1
Non-intrusive load monitoring (NILM) is a technique that uses a single sensor to measure the total power consumption of a building. Using an energy disaggregation method, the consu…
Measuring Disentanglement: A Review of Metrics
Marc-André Carbonneau, Julian Zaidi, Jonathan Boilard +1
Learning to disentangle and represent factors of variation in data is an important problem in AI. While many advances have been made to learn these representations, it is still unc…
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks
Marouane Hachimi, Georges Kaddoum, Ghyslain Gagnon +1
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently…
Early Detection for Optimal-Latency Communications in Multi-Hop Links
Diego Barragán Guerrero, Minh Au, Ghyslain Gagnon +2
Modern wireless machine-to-machine-type communications aim to provide both ultra reliability and low latency, stringent requirements that appear to be mutually exclusive. From the…
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
Marc-André Carbonneau, Eric Granger, Ghyslain Gagnon
A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some ta…
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Marc-André Carbonneau, Veronika Cheplygina, Eric Granger +1
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. Thi…
Feature Learning from Spectrograms for Assessment of Personality Traits
Marc-André Carbonneau, Eric Granger, Yazid Attabi +1
Several methods have recently been proposed to analyze speech and automatically infer the personality of the speaker. These methods often rely on prosodic and other hand crafted sp…