62 citations · 62 across the 12 of their papers we have counts for
6 papers · 1 filter
Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Yannick Lunk, Atell Yehor Krasnopolsky, Damien Garreau +1
Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer vision, saliency maps, which highlight the image region…
Inpainting Insights: Elevating Visual XAI with Photorealistic Perturbations
Josef Lindl, Mariana Chaves, Damien Garreau
The increasing complexity of state-of-the-art machine learning models has made their behavior progressively harder to interpret, spurring rapid advancements in the field of eXplain…
A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
Augustin de la Brosse, Damien Garreau, Thomas Houet +1
Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are the primary tools for this…
Beyond Mixtures and Products for Ensemble Aggregation: A Likelihood Perspective on Generalized Means
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
Density aggregation is a central problem in machine learning, for instance when combining predictions from a Deep Ensemble. The choice of aggregation remains an open question with…
Towards Understanding Steering Strength
Magamed Taimeskhanov, Samuel Vaiter, Damien Garreau
A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations. Namely, identify a well-chosen direction dependi…
When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
Diffusion models now generate high-quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well-known way to improve supervised models,…