1 citations · 1 across the 2 of their papers we have counts for
5 papers
Before Parc Fermé: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving
Luca Benfenati, Ali Azimi, Matteo Risso +3
Embodied Large Language Models (LLMs) are increasingly used as reasoning modules in robotic control pipelines to improve human-robot interaction, but their memory and generation la…
Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold
Luca Benfenati, Matteo Risso, Andrea Vannozzi +5
Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capacity and bandwidth. A common mitig…
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights
Lorenz K. Müller, Philippe Bich, Jiawei Zhuang +3
Post-training quantization has emerged as the most widely used strategy for deploying large language models at low precision. Still, current methods show perplexity degradation at…
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation
Luca Benfenati, Sofia Belloni, Alessio Burrello +6
Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show p…
BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring
Luca Benfenati, Thorir Mar Ingolfsson, Andrea Cossettini +3
This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-t…