62 citations · 62 across the 12 of their papers we have counts for
13 papers
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,…
On The Variability of Concept Activation Vectors
Julia Wenkmann, Damien Garreau
One of the most pressing challenges in artificial intelligence is to make models more transparent to their users. Recently, explainable artificial intelligence has come up with num…
MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
Kensuke Mitsuzawa, Damien Garreau
Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these…
Feature Attribution from First Principles
Magamed Taimeskhanov, Damien Garreau
Feature attribution methods are a popular approach to explain the behavior of machine learning models. They assign importance scores to each input feature, quantifying their influe…
GLEAMS: Bridging the Gap Between Local and Global Explanations
Giorgio Visani, Vincenzo Stanzione, Damien Garreau
The explainability of machine learning algorithms is crucial, and numerous methods have emerged recently. Local, post-hoc methods assign an attribution score to each feature, indic…
CAM-Based Methods Can See through Walls
Magamed Taimeskhanov, Ronan Sicre, Damien Garreau
CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights…
Are Ensembles Getting Better all the Time?
Pierre-Alexandre Mattei, Damien Garreau
Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on t…
Large sample analysis of the median heuristic
Damien Garreau, Wittawat Jitkrittum, Motonobu Kanagawa
In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels. While its empirical performances make it a safe choice under many cir…