activity
20212026
most citedLow-complexity acoustic scene classification in DCASE 2022 Challenge

22 citations · 34 across the 11 of their papers we have counts for

collaborators

11 papers

physics.ins-det2026

Energy Calibration and Performance of HPGe Detectors in the LEGEND-200 Experiment

The LEGEND Collaboration, H. Acharya, M. Agostini +259

This paper describes the energy scale procedures and germanium detectors performance in the LEGEND-200 experiment, a critical component for the first unblinding in the search for n…

cs.HC2025

Explicit modelling of subject dependency in BCI decoding

Michele Romani, Francesco Paissan, Andrea Fossà +1

Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to…

cs.LG2025

A probabilistic framework for dynamic quantization

Gabriele Santini, Francesco Paissan, Elisabetta Farella

We propose a probabilistic framework for dynamic quantization of neural networks that allows for a computationally efficient input-adaptive rescaling of the quantization parameters…

cs.SD2025

From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms

Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3

Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these deve…

cs.CV2024★ 1 cited

PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge

Manuel Barusco, Francesco Borsatti, Davide Dalle Pezze +3

Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A…

cs.CV2024

Latent Distillation for Continual Object Detection at the Edge

Francesco Pasti, Marina Ceccon, Davide Dalle Pezze +4

While numerous methods achieving remarkable performance exist in the Object Detection literature, addressing data distribution shifts remains challenging. Continual Learning (CL) o…