Wojciech Samek

A relevance-based concept atlas reveals how tissue morphology directly influences spatial transcriptomics predictions, enhancing interpretability in pathology.

Sparsifying activations in collaborative inference may cut costs, but it exposes a hidden privacy risk from the positions of those activations that could enable re-identification.

Achieving formal privacy in federated learning without sacrificing model performance, FedKT-CSD outperforms traditional methods even under stringent privacy constraints.

Selective data sharing guided by XAI can significantly boost federated learning performance, achieving higher accuracy and faster convergence even in heterogeneous environments.

AIR achieves over 18% better perplexity than previous methods while retaining 60% of the parameters, revolutionizing LLM compression efficiency.

Outer informedness in INDEQS consistently reduces forecasting error on complex graphs, outperforming traditional methods even with similar parameter counts.

Activation probes can predict future behaviors in reasoning models with up to 91% accuracy, enabling effective steering without sacrificing output quality.