53 citations · 126 across the 13 of their papers we have counts for
19 papers
Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination
Eric Nyiri, Olivier Gibaru
Machine learning methods are solving very successfully a plethora of tasks, but they have the disadvantage of not providing any information about their decision. Consequently, esti…
Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots
M. A. Simão, O. Gibaru, P. Neto
Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more peo…
Improving novelty detection with generative adversarial networks on hand gesture data
Miguel Simão, Pedro Neto, Olivier Gibaru
We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (G…
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Joris Guerin, Stephane Thiery, Eric Nyiri +2
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pr…
Semantically Meaningful View Selection
Joris Guérin, Olivier Gibaru, Eric Nyiri +2
An understanding of the nature of objects could help robots to solve both high-level abstract tasks and improve performance at lower-level concrete tasks. Although deep learning ha…
Unsupervised robotic sorting: Towards autonomous decision making robots
Joris Guérin, Stéphane Thiery, Eric Nyiri +1
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object…
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Joris Guérin, Olivier Gibaru, Eric Nyiri +1
Unlike classification, position labels cannot be assigned manually by humans. For this reason, generating supervision for precise object localization is a hard task. This paper det…
CNN features are also great at unsupervised classification
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the…
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was develope…
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
Joris Guerin, Olivier Gibaru, Eric Nyiri +1
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe fo…
spline fits via sliding window process : continuous and discrete cases
Laurent Gajny, Olivier Gibaru, Eric Nyiri
Best approximation of the Heaviside function and best approximation of multiscale univariate datasets by cubic splines have a Gibbs phenomenon. Numerical experiments…
Best approximation of Heaviside-type functions in Chebyshev and weak-Chebyshev spaces
Laurent Gajny, Olivier Gibaru, Eric Nyiri +1
In this article, we study the problem of best approximation of Heaviside-type functions in Chebyshev and weak-Chebyshev spaces. We extend the Hobby-Rice theorem into an appro…
Identification of fractional order systems using modulating functions method
Da-Yan Liu, Taous-Meriem Laleg-Kirati, Olivier Gibaru +1
The modulating functions method has been used for the identification of linear and nonlinear systems. In this paper, we generalize this method to the on-line identification of frac…
Fractional order differentiation by integration with Jacobi polynomials
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti +1
The differentiation by integration method with Jacobi polynomials was originally introduced by Mboup, Join and Fliess. This paper generalizes this method from the integer order to…
Non-asymptotic fractional order differentiators via an algebraic parametric method
Dayan Liu, Olivier Gibaru, Wilfrid Perruquetti
Recently, Mboup, Join and Fliess [27], [28] introduced non-asymptotic integer order differentiators by using an algebraic parametric estimation method [7], [8]. In this paper, in o…
Convergence Rate of the Causal Jacobi Derivative Estimator
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti
Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new par…
Parameters estimation of a noisy sinusoidal signal with time-varying amplitude
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti
In this paper, we give estimators of the frequency, amplitude and phase of a noisy sinusoidal signal with time-varying amplitude by using the algebraic parametric techniques introd…
Error analysis of a class of derivative estimators for noisy signals
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti
Recent algebraic parametric estimation techniques led to point-wise derivative estimates by using only the iterated integral of a noisy observation signal. In this paper, we extend…
Differentiation by integration with Jacobi polynomials
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti
In this paper, the numerical differentiation by integration method based on Jacobi polynomials originally introduced by Mboup, Fliess and Join is revisited in the central case wher…