15 papers
PVeRA: Probabilistic Vector-Based Random Matrix Adaptation
Leo Fillioux, Enzo Ferrante, Paul-Henry Cournède +2
Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning such models demands vast datasets…
THUNDER: Tile-level Histopathology image UNDERstanding benchmark
Pierre Marza, Leo Fillioux, Sofiène Boutaj +6
Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where…
Are foundation models for computer vision good conformal predictors?
Leo Fillioux, Julio Silva-RodrÃguez, Ismail Ben Ayed +4
Recent advances in self-supervision and contrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progres…
OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection
Kunal Mahatha, Ali Bahri, Pierre Marza +5
State space models (SSMs) have recently emerged as an alternative to transformers due to their unique ability of modeling global relationships in text with linear complexity. Howev…
SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Aris L. Moustakas +2
Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This…
Class Adaptive Conformal Training
Badr-Eddine Marani, Julio Silva-Rodriguez, Ismail Ben Ayed +3
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident…