36 citations · 77 across the 9 of their papers we have counts for
10 papers
Time and covariance smoothing for restoration of bivariate signals
Yusuf Yigit Pilavci, Pierre Palud, Julien Flamant +3
In many applications and physical phenomena, bivariate signals are polarized, i.e. they trace an elliptical trajectory over time when viewed in the 2D planes of their two component…
Denoising bivariate signals via smoothing and polarization priors
Yusuf Yigit Pilavci, Jérémie Boulanger, Pierre-Antoine Thouvenin +1
We propose two formulations to leverage the geometric properties of bivariate signals for dealing with the denoising problem. In doing so, we use the instantaneous Stokes parameter…
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests
Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme +1
The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its dir…
Variance reduction in stochastic methods for large-scale regularised least-squares problems
Yusuf Pilavcı, Pierre-Olivier Amblard, Simon Barthelmé +1
Large dimensional least-squares and regularised least-squares problems are expensive to solve. There exist many approximate techniques, some deterministic (like conjugate gradient)…
Graph Tikhonov Regularization and Interpolation via Random Spanning Forests
Yusuf Pilavci, Pierre-Olivier Amblard, Simon Barthelme +1
Novel Monte Carlo estimators are proposed to solve both the Tikhonov regularization (TR) and the interpolation problems on graphs. These estimators are based on random spanning for…
Graph Domain Adaptation with Localized Graph Signal Representations
Yusuf Yigit Pilavci, Eylem Tugce Guneyi, Cemil Cengiz +1
In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we…
Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in Neuroimaging
Yusuf Pilavci, Nicolas Farrugia
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains. Exploiting transformations that are defined on gr…
Smoothing graph signals via random spanning forests
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé +1
Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo…
Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots
Mehmet Turan, Yusuf Yigit Pilavci, Ipek Ganiyusufoglu +3
Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be co…
A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot
Mehmet Turan, Yusuf Yigit Pilavci, Redhwan Jamiruddin +3
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI…