Smoothing graph signals via random spanning forests
Yusuf Pilavci, Pierre-Olivier Amblard, Simon Barthelme, Nicolas Tremblay. Smoothing graph signals via random spanning forests. ICASSP 2020, May 2020, barcelone, Spain. ⟨ hal-02319175v2 ⟩
Approximating Spectral Clustering via Sampling: a Review
Nicolas Tremblay, Andreas Loukas. Approximating Spectral Clustering via Sampling: a Review. Sampling Techniques for Supervised or Unsupervised Tasks, 2020. ⟨ hal-02468312 ⟩
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay. Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs. Thirty-third Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada. ⟨ hal-02429525 ⟩
Determinantal Point Processes for Coresets
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard. Determinantal Point Processes for Coresets. Journal of Machine Learning Research, Microtome Publishing, 2019. ⟨ hal-01741533v2 ⟩
Fourier could be a Data Scientist: from Graph Fourier Transform to Signal Processing on Graphs
Benjamin Ricaud, Pierre Borgnat, Nicolas Tremblay, Paulo Gonçalves, Pierre Vandergheynst. Fourier could be a Data Scientist: from Graph Fourier Transform to Signal Processing on Graphs. Comptes Rendus Physique, Elsevier Masson, 2019, pp.474-488. ⟨ 10.1016/j.crhy.2019.08.003 ⟩. ⟨ hal-02304584 ⟩
Classification spectrale par la laplacienne déformée dans des graphes réalistes
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay. Classification spectrale par la laplacienne déformée dans des graphes réalistes. XXVIIème colloque GRETSI (GRETSI 2019), Aug 2019, lille, France. ⟨ hal-02153901 ⟩
Estimating the inverse trace using random forests on graphs
Simon Barthelme, Nicolas Tremblay, Alexandre Gaudilliere, Luca Avena, Pierre-Olivier Amblard. Estimating the inverse trace using random forests on graphs. XXVIIème colloque GRETSI (GRETSI 2019), Aug 2019, Lille, France. ⟨ hal-02319194 ⟩
Guillaume Becq, Nagham Badreddine, Nicolas Tremblay, Florence Appaix, Gisela Zalcman, et al.. Classification de types de neurones à partir de signaux calciques. Gretsi 2019, Aug 2019, Lille, France. ⟨ hal-02528364 ⟩
Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes
Simon Barthelme, Pierre-Olivier Amblard, Nicolas Tremblay. Asymptotic Equivalence of Fixed-size and Varying-size Determinantal Point Processes. Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, In press, 25 (4B), pp.3555-3589. ⟨ 10.3150/18-BEJ1102 ⟩. ⟨ hal-02086028 ⟩
Design of graph filters and filterbanks
Nicolas Tremblay, Paulo Gonçalves, Pierre Borgnat. Design of graph filters and filterbanks. Petar M. Djurić; Cédric Richard. Cooperative and Graph Signal Processing, Academic Press, pp.299-324, 2018, 978-0-12-813677-5. ⟨ 10.1016/B978-0-12-813677-5.00011-0 ⟩. ⟨ hal-01675375 ⟩
Prénom NOM | Date d'entrée en thèse | Sujet | Ecole doctorale |
CORDONNIER Matthieu | 01/10/2021 | Propriétés des réseaux de neurones sur grands graphes aléatoires | EEATS |
JAQUARD Hugo | 01/10/2021 | Processus ponctuels déterminantaux pour l’algèbre linéaire randomisée sur graphes | EEATS |
PILAVCI Yusuf Yigit | 01/10/2019 | L’algorithme de Wilson pour l’algèbre linéaire randomisée | EEATS |
Grenoble Images Parole Signal Automatique laboratoire
UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal