Maximilian Dreyer

ICON Decomposition : Multivariate Concept-Level Explanations of Deep Representations for Model Auditing

ResLRP: The Role of Residual Cancellation in Attribution Instability in Vision Transformers

Residual-aware Layer-wise Relevance Propagation (ResLRP) is introduced, a simple extension of LRP whose propagation rules explicitly account for cancellations in residual branches, are exactly conservative, and provably bound relevance explosion.

Safety Signals to Verify NetOps Agents with Action-Level Granularity

Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. While agents have proven their value in incident summarization and telemetry signal extraction, their effectiveness as autonomous control-loop engines heavily relies on the...

ICON Decomposition: Auditing Deep Neural Networks with Multivariate Variance-based Concept-level Explanations

CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.