Computational Platform

Computational Platform @ LRE

In Brief

  • Name: CPL - Computing Platform @ LRE
  • Scientific challenges: Design and evaluation of explainable and scalable learning models
  • Technological challenges: Deployment of data processing pipelines applied to LRE research areas: learning, image processing, security, human sciences

Objectives

Support research for LRE members:

  • Unify and centralize computing resources accessible to permanent, temporary, and associated LRE researchers, through personal research or collaborative projects
  • Conduct controlled and reproducible experiments

Staff

  • Scientific Lead: Marc Plantevit
  • Technical Lead: Daniel Stan
  • Involved teams: IA, TIRF, MNSHS, SECUSYS
  • System Administrator: Lucas Dextreit-Colombel

Resources

  • Node9 (Configuration: GeForce 1080 Ti – 10,609 TFlops single precision, GeForce 1080 – 8,228 TFlops SP, Titan X (Pascal) – 10,157 TFlops SP, Quadro P6000)
  • Node10 (Configuration: 3xRTX8000, 16,312 TFlops SP each)
  • GPU-workers-[012]: 3xA40, 37.42 TFlops SP each
  • And more to come ...

Dissemination

Scientific Production

Publications - Journals - 2024-2025

  • G. Quaglia, G. Tochon, V. Lainey, and R. D. T. Strauss, “Deep learning based detection and classification of small bright sources on ISS images from the Cassini mission and application to Saturn’s outer magnetosphere,” Monthly Notices of the Royal Astronomical Society, pp. 1–15, Oct. 202
  • T. Lepage and R. Dehak, “Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling,” IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 2932–2945, 2025, doi: 10.1109/TASLPRO.2025.3587462.
  • A. Kamal, A. Ragno, M. Plantevit, and C. Robardet, “Leveraging internal representations of GNNs with Shapley Values,” Data Mining and Knowledge Discovery, pp. 1–39, Sep. 2025
  • A. Frion, L. Drumetz, M. Dalla Mura, G. Tochon, and A. El Bey Aïssa-El-Bey, “Augmented Invertible Koopman Autoencoder for long-term time series forecasting,” Transactions on Machine Learning Research, pp. 1–28, May 2025.
  • E. Carlinet, Q. Kaci, and N. Blin, “An Alpha-Tree Algorithm for Massively Parallel Architectures,” IEEE Transactions on Image Processing, vol. 34, pp. 4402–4413, Jul. 2025, doi: 10.1109/TIP.2025.3586495.
  • L. Veyrin-Forrer, A. Kamal, S. Duffner, M. Plantevit, and C. Robardet, “On GNN Explainability with Activation Rules,” Data Mining and Knowledge Discovery, vol. 38, no. 5, pp. 3227–3261, Oct. 2024, doi: 10.1007/s10618-022-00870-z.
  • C. H. Sudre et al., “Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021,” Medical Image Analysis, vol. 91, p. 103029, Jan. 2024, doi: 10.1016/j.media.2023.103029.
  • M. Puren, F. Lebreton, A. Pellet, and P. Vernus, “From parliamentary history to digital and computational history: a NLP-friendly TEI model for historical parliamentary proceedings,” Digital Scholarship in the Humanities, vol. 40, no. Supplement_1, pp. i75–i86, Nov. 2024, doi: 10.1093/llc/fqae071.
  • M. Puren and P. Vernus, “Conceptual Modeling of European Silk Heritage with the SILKNOW Data Model and Extension,” Digital Humanities Quarterly, vol. 18, no. 3, Aug. 2024.
  • M. Moranges, A. Fournel, M. Thévenet, M. Plantevit, and M. Bensafi, “Using Exceptional Attributed Subgraph Mining to Explore Interindividual Variability in Odor Pleasantness Processing in the Piriform Cortex and Amygdala,” Intelligent Computing, vol. 3, no. 86, pp. 1–16, Aug. 2024, doi: 10.34133/icomputing.0086.
  • C. Mazini-Rodriguez, N. Boutry, and L. Najman, “Transforming gradient-based techniques into interpretable methods,” Pattern Recognition Letters, Jun. 2024, doi: 10.1016/j.patrec.2024.06.006.
  • C. Mazini-Rodrigues, N. Boutry, and L. Najman, “Unsupervised discovery of Interpretable Visual Concepts,” Information Sciences, Jan. 2024, doi: 10.1016/j.ins.2024.120159.
  • K. A. J. Koua, C. T. Diop, L. Diop, and M. Diop, “Enhanced neonatal screening for sickle cell disease: Human-guided deep learning with CNN on isoelectric focusing images,” Journal of Infrastructure, Policy and Development, vol. 8, no. 9, p. 6121, 2024, doi: 10.24294/jipd.v8i9.6121
  • J.-B. Guimbaud et al., “Machine learning-based health environmental-clinical risk scores in European children,” Communications Medicine, vol. 4, no. 98, May 2024, doi: 10.1038/s43856-024-00513-y.
  • A. Frion, L. Drumetz, M. Dalla Mura, G. Tochon, and A. A. El Bey, “Neural Koopman prior for data assimilation,” IEEE Transactions on Signal Processing, vol. 72, pp. 4191–4206, Jun. 2024, doi: 10.1109/TSP.2024.3416828.
  • Y. Chen, J. Chazalon, E. Carlinet, M. Ô. V. Ngọc, C. Mallet, and J. Perret, “Automatic vectorization of historical maps: A benchmark,” PLOS ONE, vol. 19, no. 2, pp. 1–23, Feb. 2024, doi: 10.1371/journal.pone.0298217

Collaborative Projects

  • Socio-economic Partners : Bilbiothèque Nationale de France (BNF; Mezzano Project)
  • Enterprise partners : AriadNext (CIFRE), APL Datacenter (CIFRE), Evoliz (bilateral)
  • Projects : APATE (ANR, 2022-2025), PANDORA (ANR, 2025-2028), DECIDON (ANR, 2025-2028)