7 papers
DINOv3
Oriane Siméoni, Huy V. Vo, Maximilian Seitzer +23
Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. B…
Cluster and Predict Latent Patches for Improved Masked Image Modeling
Timothée Darcet, Federico Baldassarre, Maxime Oquab +2
Masked Image Modeling (MIM) offers a promising approach to self-supervised representation learning, however existing MIM models still lag behind the state-of-the-art. In this paper…
Counterfactual Learning of Stochastic Policies with Continuous Actions
Houssam Zenati, Alberto Bietti, Matthieu Martin +3
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of le…
Functional Bilevel Optimization for Machine Learning
Ieva Petrulionyte, Julien Mairal, Michael Arbel
In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. Thes…
Combining statistical learning with deep learning for improved exoplanet detection and characterization
Olivier Flasseur, Théo Bodrito, Julien Mairal +3
In direct imaging at high contrast, the bright glare produced by the host star makes the detection and the characterization of sub-stellar companions particularly challenging. In s…
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Behnood Rasti, Alexandre Zouaoui, Julien Mairal +1
Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and inti…