Luca Benini

Recursive refinement unlocks a 44% relative gain in spatial context prediction accuracy for VLMs, even with minimal trainable parameters.

Recursive models achieve state-of-the-art multivariate time-series classification by integrating learned temporal tokenization with parameter-efficient, weight-tied state refinement.

Achieving accurate center of mass estimation from a single smartphone camera could revolutionize athlete performance analysis in real-world settings.

Achieving an 89.2% area reduction in fault monitoring for mixed-criticality SoCs could revolutionize the design of safety-critical systems.

S-CEReBrO processes EEG signals 100 times longer than traditional methods while slashing memory usage by 55%.

Foundation models for EEG can now be 377x more efficient and handle 12x longer sequences, thanks to a novel Mamba-based architecture that also cracks the code for handling variable electrode setups.