Wojciech Samek

The results support the conclusion: foundation-model supervision can amplify a fixed manual annotation budget into a substantially larger training set and yield a compact, deployable edge model.

The results suggest that gradient-based attribution can identify data responsible for subliminal learning in some settings, but that some approximations are more reliable than others.

A searchable catalog of over a thousand AI model findings could revolutionize how researchers access and build upon existing knowledge in the field.

Transforming a deterministic weather model into a probabilistic one reveals how uncertainty can significantly enhance forecasting accuracy and transparency.

ICON decomposition reveals the true reliance of deep models on concepts, debunking misleading correlations that traditional methods often overlook.

Incentivizing honest participation in federated learning is now possible without ground truth labels, even when some participants are trying to game the system.

Steering isn't just a trick; it's a fundamentally different way to adapt language models, offering localized, reversible control that traditional fine-tuning can't match.

Blockchain-based federated learning can be made practical by using multi-task peer prediction to overcome the computational bottleneck of contribution measurement.