Ban lãnh đạo nghiên cứu : Matthieu CORD

Đồng hướng dẫn : SCHWANDER Olivier

Deep Learning and Information Geometry for Time-Series Classification

This work tackles the classification of structured time series, in particular micro-Doppler radar signals from non-cooperative drone targets. We first dwell upon the rich internal structure of such signals, which births a variety of different, yet relatable input representations which in turn may yield a variety of different learning models, namely convolutional and SPD neural networks. We then show how we can adapt known models to the structure of the data through the scope of information geometry, and go further by building novel schemes for the classification of such data. We finally demonstrate a full-classification pipeline making use of all aforementioned structures involved in data formation and classification, and present results thereupon on multiple datasets of real and synthetic data.

Bài báo khoa học 2018-2020

• 2020
  • D. Brooks : “Apprentissage profond et géométrie de l’information pour la classification des séries chronologiques”, luận án, bảo vệ luận án 07/03/2020, ban lãnh đạo nghiên cứu Cord, Matthieu, Đồng hướng dẫn : Schwander, Olivier (2020)
  • N. Miolane, N. Guigui, A. Le Brigant, J. Mathe, B. Hou, Y. Thanwerdas, S. Heyder, O. Peltre, N. Koep, H. Zaatiti, H. Hajri, Y. Cabanes, Th. Gerald, P. Chauchat, Ch. Shewmake, D. Brooks, B. Kainz, C. Donnat, S. Holmes, X. Pennec : “Geomstats: A Python Package for Riemannian Geometry in Machine Learning”, Journal of Machine Learning Research, vol. 21 (223), pp. 1-9, (Microtome Publishing) (2020)
  • N. Miolane, N. Guigui, H. Zaatiti, Ch. Shewmake, H. Hajri, D. Brooks, A. Le Brigant, J. Mathe, B. Hou, Y. Thanwerdas, S. Heyder, O. Peltre, N. Koep, Y. Cabanes, Th. Gerald, P. Chauchat, B. Kainz, C. Donnat, S. Holmes, X. Pennec : “Introduction to Geometric Learning in Python with Geomstats”, SciPy 2020 - 19^th Python in Science Conference, Austin, Texas, United States, pp. 48-57 (2020)
• 2019
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Riemannian batch normalization for SPD neural networks”, Thirty-third Annual Conference on Neural Information Processing Systems., Vancouver, Canada (2019)
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “A Hermitian Positive Definite neural network for micro-Doppler complex covariance processing”, International Radar Conference, Toulon, France (2019)
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Second-order networks in PyTorch”, GSI 2019 - 4^th International Conference on Geometric Science of Information, vol. 11712, Lecture Notes in Computer Science, Toulouse, France, pp. 751-758, (Springer) (2019)
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Complex-valued neural networks for fully-temporal micro-Doppler classification”, 2019 20^th International Radar Symposium (IRS), 2019 20^th International Radar Symposium (IRS), Ulm, Germany, (IEEE) (2019)
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Exploring Complex Time-series Representations for Riemannian Machine Learning of Radar Data”, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, pp. 3672-3676, (IEEE) (2019)
• 2018
  • D. Brooks, O. Schwander, F. Barbaresco, J.‑Y. Schneider, M. Cord : “Temporal Deep Learning for Drone Micro-Doppler Classification”, IRS 2018 - 19^th International Radar Symposium, Bonn, Germany, (IEEE) (2018)