1 code implementation • 21 Feb 2024 • Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos, Merwan Barlier
This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent.
no code implementations • 21 Feb 2024 • Paul Daoudi, Bojan Mavkov, Bogdan Robu, Christophe Prieur, Emmanuel Witrant, Merwan Barlier, Ludovic Dos Santos
This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment.
no code implementations • 24 Dec 2023 • Paul Daoudi, Christophe Prieur, Bogdan Robu, Merwan Barlier, Ludovic Dos Santos
In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer.
no code implementations • 20 Oct 2022 • Houssem Meghnoudj, Bogdan Robu, Mazen Alamir
In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals.
1 code implementation • 19 Mar 2021 • Zilong Zhao, Robert Birke, Rui Han, Bogdan Robu, Sara Bouchenak, Sonia Ben Mokhtar, Lydia Y. Chen
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.
no code implementations • 20 Mar 2020 • Zilong Zhao, Sophie Cerf, Bogdan Robu, Nicolas Marchand
During its training the learning rate and the gradient are two key factors to tune for influencing the convergence speed of the model.
no code implementations • 18 Nov 2019 • Zilong Zhao, Sophie Cerf, Bogdan Robu, Nicolas Marchand
Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows.
no code implementations • 11 Nov 2019 • Zilong Zhao, Robert Birke, Rui Han, Bogdan Robu, Sara Bouchenak, Sonia Ben Mokhtar, Lydia Y. Chen
Classification algorithms have been widely adopted to detect anomalies for various systems, e. g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i. e., features and labels are correctly set.