Hugues Kouakou, José Henrique de M Goulart, Raffaele Vitale, Thomas Oberlin, David Rousseau, et al.. On-the-fly spectral unmixing for real-time hyperspectral data analysis. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Dec 2024, Helsinki, Finland. ⟨hal-04774759⟩

Hugues Kouakou, José Henrique de M Goulart, Raffaele Vitale, Thomas Oberlin, David Rousseau, et al.. On-the-fly spectral unmixing based on Kalman filtering. Chemometrics and Intelligent Laboratory Systems, 2024, 255, pp.105252. ⟨10.1016/j.chemolab.2024.105252⟩. ⟨hal-04774748⟩

Malek Ellouze, Romain Tajan, Camille Leroux, Christophe Jégo, Charly Poulliat. Computing the Low-Weight codewords of Punctured and Shortened Pre-Transformed polar Codes. 2024. ⟨hal-04770530v2⟩

Nathanaël Munier, Emmanuel Soubies, Pierre Weiss. Exploring instabilities of inverse problem solvers with low-dimensional manifolds. 2024. ⟨hal-04753218⟩

Nathanaël Munier, Emmanuel Soubies, Pierre Weiss. Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds. 2024. ⟨hal-04744486⟩

Elhadji C. Faye, Mame Diarra Fall, Aladine Chetouani, Nicolas Dobigeon. Bayesian formulation of regularization by denoising - Model and Monte Carlo sampling. IEEE International Workshop on Multimedia Signal Processing (MMSP), Oct 2024, West Lafayette, IN, United States. ⟨hal-04774763⟩

Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais. Normalizing flow sampling with Langevin dynamics in the latent space. Machine Learning, 2024, ⟨10.1007/s10994-024-06623-x⟩. ⟨hal-04710673⟩

Mhamed Essafri, Luca Calatroni, Emmanuel Soubies. On L0 Bregman-Relaxations for Kullback-Leibler Sparse Regression. 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), Sep 2024, London, France. pp.1-6, ⟨10.1109/MLSP58920.2024.10734806⟩. ⟨hal-04771394⟩

Minh Hai Nguyen, Pierre Weiss. Comparing Plug-and-Play and Unrolled networks. 2024. ⟨hal-04703008⟩

Jin-Ju Wang, Nicolas Dobigeon, Marie Chabert, Ding-Cheng Wang, Ting-Zhu Huang, et al.. CD-GAN: A robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors. Information Fusion, 2024, 107, pp.102313. ⟨10.1016/j.inffus.2024.102313⟩. ⟨hal-04483118⟩