Mes recherches portent sur la théorie des probabilités. Plus précisément, voici les sujets sur lesquels j'ai publié des articles :

• l'apprentissage statistique, les réseaux neuronaux, les modèles de mémoire associative, le modèle de Hopfield.
• l'imagerie médicale.
• les sciences actuarielles.
• la physique statistique.
• les algorithmes stochastiques, les méthodes de Monte Carlo.
• la théorie de la communication multi-utilisateurs.
• les marches aléatoires.

Voici la liste complète de mes publications (l'autre 'onglet "Publications" est une extraction automatique de HAL) :

Prépublications :

• C. Vazia, A. Bousse, B. Vedel, F. Vermet, Z. Wang, Th.Dassow, J.-P. Tasu, D. Visvikis, J. Froment. Diffusion posterior sampling for synergistic reconstruction in spectral computed tomography. (2024)
   
• P. Soto Vega, V. Bourbonne, W. Marchadour, G. Andrade-Miranda, F. Lucia, M. Rehn, U. Schick, D. Visvikis, F. Vermet, M. Hatt, Prediction of Acute Pulmonary Toxicity Events with 3D Convolutional Neural Networks from Radiotherapy Dose Maps. (2024)
   
• A. Charpentier, M. Moriah, F. Vermet, Measuring and Mitigating Biases in Motor Insurance Pricing. (2023) (arXiv:2311.11900)
   
• C. Vazia, A. Bousse, B. Vedel, F. Vermet, Z. Wang, Th.Dassow, J.-P. Tasu, D. Visvikis, J. Froment. Spectral diffusion posterior sampling for synergistic reconstruction in spectral computed tomography. (2023) (accepté à la conférence ISBI2024)
   
• A. Charpentier, L. Kouakou, M. Löwe, Ph. Ratz, F. Vermet, Collaborative Insurance Sustainability and Network Structure. (2021) (arXiv:2107.02764)
   

Articles de recherche publiés :

• N. E. Bekri, L. Drumetz, F. Vermet, Time Changed Normalizing Flows for Accurate SDE Modeling, ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 6395-6399 (2024) (doi: 10.1109/ICASSP48485.2024.10446131)
   
• Z. Wang, A. Bousse, F. Vermet, J. Froment, B. Vedel, A. Perelli,  J.-P. Tasu, D. Visvikis. Uconnect: Synergistic Spectral CT Reconstruction with U-Nets Connecting the Energy bins.  IEEE Transactions on Radiation and Plasma Medical Sciences, 8(2), 222-233 (2024) (arXiv:2311.00666)
   
• L. Drumetz, L., A. Reiffers-Masson, N. El Bekri, F. Vermet, Geometry-Preserving Lie Group Integrators for Differential Equations on the Manifold of Symmetric Positive Definite Matrices. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2023. Lecture Notes in Computer Science, vol 14072. Springer, Cham. (https://doi.org/10.1007/978-3-031-38299-4_45) (arxiv:2210.08842)
   
• D. Delcaillau, A. Ly, A. Papp, F. Vermet, Model Transparency and Interpretability : Survey and Application to the Insurance Industry.  Eur. Actuar. J., 12, 443-484 (2022) (https://doi.org/10.1007/s13385-022-00328-y) (arXiv:2209.00562) 
   
• Z. Wang, A. Bousse, N.J. Pinton, J. Froment, F. Vermet, B. Vedel, J.-P. Tasu, D. Visvikis, Synergistic Multi-Energy CT Reconstruction with a Deep Penalty “Connecting the Energies”, 2022 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector (RTSD) Conference, IEEE, novembre 2022, Milan, IT, ⟨hal-03955092⟩
   
• P. L. Brocki, W. Marchadour, J. Maison, B. Badic, P. Papadimitroulas, M. Hatt, F. Vermet, N. Ch. Chung, Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2022. Lecture Notes in Computer Science(), vol 13283. Springer, Cham (https://doi.org/10.1007/978-3-031-15565-9_1) (arXiv:2209.15398) 
   
• Th. Giraudon, V. Gripon, M. Löwe, F. Vermet, Towards an Intrinsic Definition of Robustness for a Classifier. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4015-4019 (2021) (doi: 10.1109/ICASSP39728.2021.9414573) (arXiv:2006.05095) (https://2021.ieeeicassp.org/)
   
• P. Papadimitroulas, L. Brocki, N. C. Chung, W. Marchadour, F. Vermet, L. Gaubert, V. Eleftheriadis, D. Plachouris, D. Visvikis, G. C. Kagadis, M. Hatt, Artificial Intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Physica Medica, 83, 108-121 (2021) (https://doi.org/10.1016/j.ejmp.2021.03.009) 
   
• V. Gripon, M. Löwe, F. Vermet, Some Remarks on Replicated Simulated Annealing.   J. Stat. Phys. 182, 51 (2021) (https://doi.org/10.1007/s10955-021-02727-z) (arXiv:2009.14702)
   
• M. Löwe, K. Schubert, F. Vermet, Multi-group Binary Choice with Social Interaction and a Random Communication Structure - a Random Graph Approach.   Physica A: Stat. Mech. Appl. 556, 124735 (2020) (arXiv:1904.11890)
   
• V. Gripon, G. B. Hacene, M. Löwe, F. Vermet, Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation.  IEEE ICASSP 2018, 2966-2970 (2018) (arXiv:1710.08637)
   
• V. Gripon, M. Löwe, F. Vermet, Associative Memories to Accelerate Approximate Nearest Neighbor Search.  Appl. Sci. 8(9), 1676 (2018) (Open Access)
   
• M. Demircigil, J. Heusel, M. Löwe, S. Upgang, F. Vermet, On a model of associative memory with huge storage capacity.  J. Stat. Phys. 168 (2), 288-299 (2017) (arXiv:1702.01929)
   
• V. Gripon, J. Heusel, M. Löwe, F. Vermet, A comparative study of sparse associative memories. J. Stat. Phys. 164 (1), 105-129  (2016) (arXiv:1512.08892)
   
• J. Heusel, M. Löwe, F. Vermet, On the capacity of a new model of associative memory based on neural cliques. Stat. & Prob. Lett., 106, 256-261 (2015) (arXiv:1411.1224)
   
• M. Löwe, F. Vermet, Capacity of an associative memory model on random graph architectures. Bernoulli 21 (3), 1884-1910 (2015)  (arXiv:1303.4542)
   
• M. Ebbers, H. Knöpfel, M. Löwe,  F. Vermet,  Mixing times for the Swapping Algorithm on the Blume-Emery-Griffiths Model. Random Structures & Algorithms 45 (1), 38-77 (2014) (arXiv:1206.4162)
   
• C. Wright, R. B. Scott, D. Furnival, P. Ailliot, F. Vermet, Global Observations of Ocean-Bottom Subinertial Current Dissipation. Journal of Physical Oceanography 43 (2), 402-417 (2013)
   
• M. Löwe,  F. Vermet,  The Hopfield model on a sparse Erdö-Renyi graph.  J. Stat. Phys. 143, 205-214 (2011) 
   
• M. Löwe, F. Vermet, The swapping algorithm for the Hopfield model with two patterns. Stochastic Process. Appl. 119 (10), 3471-3493 (2009)
   
• M. Löwe, F. Vermet, Capacity bounds for the CDMA system and a neural network : a moderate deviations approach.  ESAIM Probab. Stat. 13, 343- 362 (2009)
   
• M. Löwe, F. Vermet, The Capacity of q-state Potts neural networks with Parallel Retrieval Dynamics.  Stat. & Prob. Lett. 77, 1505-1514 (2007)
   
• R. van der Hofstad, M. Löwe, F. Vermet,  The effect of system load on the existence of bit-errors in CDMA with and without parallel interference cancelation.  IEEE Transactions on Information Theory 52, 4733-4741 (2006)
   
• M. Löwe, F. Vermet, The storage capacity of the Hopfield model and moderate deviations. Stat. & Prob. Lett., 75, 237-248 (2005)
   
• M. Löwe, F. Vermet, The storage capacity of the Blume-Emery-Griffiths neural network. J. Phys. A : Math. Gen., 38 (16), 3483-3503 (2005)
   
• F. Vermet, Phase transition and law of large numbers for a non-symmetric one-dimensional random walk with self-interactions. J. Appl. Prob., 35, 55-63 (1998)
   
•   F. Vermet, Transition de phase et vitesse de fuite pour une mesure discrète de Edwards non symétrique sur Z. (French) (Phase transition and escape speed for a nonsymmetric discrete Edwards measure on Z) C. R. Acad. Sci. Paris Sér. I Math. 322 (1996), no. 6, 567-570 (1996)
   
• F. Vermet, Discrétisation d'une équation différentielle stochastique dont les coefficients ne dépendent pas du temps et calcul approché d'espérances de fonctionnelles de la solution. (French) (Discretization of a stochastic differential equation whose coefficients are not time-dependent, and rough estimate of the expectations of functionals of the solution) Fascicule de probabilités, 65 pp., Publ. Inst. Rech. Math. Rennes, Univ. Rennes I, Rennes (1992) (pdf)
   
• F. Vermet, Convergence de la variance de l'énergie libre pour le modèle de Hopfield. (French) |LS|Convergence of the variance of the free energy in the Hopfield model|RS| C. R. Acad. Sci. Paris Sér. I Math. 315 (1992), no. 9, 1001-1004 (1992)

Habilitation à Diriger des Recherches : 

• Etude probabiliste de modèles neuronaux de mémoire associative et d'algorithmes utilisés en physique statistique et data science.  (pdf)
  Université de Bretagne Occidentale, Brest,  France (2019)

Thèse de Doctorat :  

• Etude asymptotique d'un réseau neuronal : le modèe de mémoire associative de Hopfield. Université de Rennes 1,  France (1994) http://tel.archives-ouvertes.fr/tel-00598243/fr/