no code implementations • 25 Nov 2022 • Ricardo Augusto Borsoi, Isabell Lehmann, Mohammad Abu Baker Siddique Akhonda, Vince Calhoun, Konstantin Usevich, David Brie, Tülay Adalı
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data.
no code implementations • 26 Jun 2022 • Julien Flamant, Konstantin Usevich, Marianne Clausel, David Brie
This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval problems.
no code implementations • 10 Jun 2022 • Jonathan Gillard, Konstantin Usevich
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.
no code implementations • 20 Sep 2021 • Konstantin Usevich, Philippe Dreesen, Mariya Ishteva
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels.
no code implementations • 25 Jun 2021 • Yassine Zniyed, Konstantin Usevich, Sebastian Miron, David Brie
Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN.
no code implementations • 30 Jun 2020 • Ricardo Augusto Borsoi, Clémence Prévost, Konstantin Usevich, David Brie, José Carlos Moreira Bermudez, Cédric Richard
In this paper, we consider the image fusion problem while accounting for both spatially and spectrally localized changes in an additive model.
no code implementations • 22 Feb 2018 • Jonathan Gillard, Konstantin Usevich
In this paper we consider the low-rank matrix completion problem with specific application to forecasting in time series analysis.
no code implementations • 4 Mar 2016 • Pierre Comon, Yang Qi, Konstantin Usevich
In this paper, we study a polynomial decomposition model that arises in problems of system identification, signal processing and machine learning.
1 code implementation • 6 Dec 2014 • Konstantin Usevich, Ivan Markovsky
In this paper, we present new results on invariance properties of the adjusted least squares estimator and an improved algorithm for computing the estimator for an arbitrary set of monomials in the polynomial equation.