4 papers · 1 filter
A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
Milad Leyli-Abadi, Lucas Thil, Sebastien Razakarivony +2
Estimating the health state of turbofan engines is a challenging ill-posed inverse problem, hindered by sparse sensing and complex nonlinear thermodynamics. Research in this area r…
Universal hidden monotonic trend estimation with contrastive learning
Edouard Pineau, Sébastien Razakarivony
In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a…
Time Series Source Separation with Slow Flows
Edouard Pineau, Sébastien Razakarivony, Thomas Bonald
In this paper, we show that slow feature analysis (SFA), a common time series decomposition method, naturally fits into the flow-based models (FBM) framework, a type of invertible…
Generalized mean shift with triangular kernel profile
Sébastien Razakarivony, Axel Barrau
The mean shift algorithm is a popular way to find modes of some probability density functions taking a specific kernel-based shape, used for clustering or visual tracking. Since it…