activity
20192024
most citedEnhancing Robustness of On-line Learning Models on Highly Noisy Data

11 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Enhancing Reinforcement Learning Agents with Local Guides

Paul Daoudi, Bogdan Robu, Christophe Prieur +2

This paper addresses the problem of integrating local guide policies into a Reinforcement Learning agent. For this, we show how to adapt existing algorithms to this setting before…

eess.SY2022

Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis

Houssem Meghnoudj, Bogdan Robu, Mazen Alamir

In this study we focus on the diagnosis of Parkinson's Disease (PD) based on electroencephalogram (EEG) signals. We propose a new approach inspired by the functioning of the brain…

cs.LG2021★ 11 cited

Enhancing Robustness of On-line Learning Models on Highly Noisy Data

Zilong Zhao, Robert Birke, Rui Han +4

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…

cs.LG2020★ 6 cited

Event-Based Control for Online Training of Neural Networks

Zilong Zhao, Sophie Cerf, Bogdan Robu +1

Convolutional Neural Network (CNN) has become the most used method for image classification tasks. During its training the learning rate and the gradient are two key factors to tun…

cs.LG2019

Feedback Control for Online Training of Neural Networks

Zilong Zhao, Sophie Cerf, Bogdan Robu +1

Convolutional neural networks (CNNs) are commonly used for image classification tasks, raising the challenge of their application on data flows. During their training, adaptation i…

cs.LG2019★ 1 cited

RAD: On-line Anomaly Detection for Highly Unreliable Data

Zilong Zhao, Robert Birke, Rui Han +4

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…