11 citations · 47 across the 48 of their papers we have counts for
4 papers · 1 filter
Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations
Maximilian Dreyer, Reduan Achtibat, Wojciech Samek +1
Ensuring both transparency and safety is critical when deploying Deep Neural Networks (DNNs) in high-risk applications, such as medicine. The field of explainable AI (XAI) has prop…
Human-Centered Evaluation of XAI Methods
Karam Dawoud, Wojciech Samek, Peter Eisert +2
In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called "black boxes" in deep learning.…
Generative Fractional Diffusion Models
Gabriel Nobis, Maximilian Springenberg, Marco Aversa +11
We introduce the first continuous-time score-based generative model that leverages fractional diffusion processes for its underlying dynamics. Although diffusion models have excell…
From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space
Maximilian Dreyer, Frederik Pahde, Christopher J. Anders +2
Deep Neural Networks are prone to learning spurious correlations embedded in the training data, leading to potentially biased predictions. This poses risks when deploying these mod…