4 citations · 14 across the 10 of their papers we have counts for
11 papers
Safety Signals to Verify NetOps Agents with Action-Level Granularity
Tobias Labarta, Frederik Pahde, Novak Boškov +6
Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While agents have proven their v…
ICON decomposition: Multivariate concept auditing reveals shortcuts that neural networks rely on
Roshan Prakash Rane, Marco Simnacher, Manuel Pfeuffer +7
Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Before deployment, models should therefore be audited for reliance on concepts such…
Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations
Eren Erogullari, Sebastian Lapuschkin, Wojciech Samek +1
Concept Activation Vectors (CAVs) are widely used to model human-understandable concepts as directions within the latent space of neural networks. They are trained by identifying d…
Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data
Frederik Pahde, Thomas Wiegand, Sebastian Lapuschkin +1
Deep neural networks are increasingly employed in high-stakes medical applications, despite their tendency for shortcut learning in the presence of spurious correlations, which can…
Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization
Rohan Reddy Mekala, Frederik Pahde, Simon Baur +11
In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical…
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification
Christian Tinauer, Anna Damulina, Maximilian Sackl +9
Motivation. While recent studies show high accuracy in the classification of Alzheimer's disease using deep neural networks, the underlying learned concepts have not been investiga…
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde +2
Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approa…
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…
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
Frederik Pahde, Maximilian Dreyer, Wojciech Samek +1
State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-makin…
Optimizing Explanations by Network Canonization and Hyperparameter Search
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder +2
Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being ap…
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde, Maximilian Dreyer, Leander Weber +5
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable co…