24 citations · 29 across the 9 of their papers we have counts for
10 papers
GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement
Emilien Guandalino, Lorenz K. Müller, Beatrice Alessandra Motetti +2
With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, q…
Wyvern: An Agentic Framework for Generating Grounded Multimodal Reports
Beatrice Alessandra Motetti, Emilien Guandalino, Daniele Jahier Pagliari +4
In the current artificial intelligence-driven innovation era, the pace of knowledge growth is accelerating, and is hard to keep up with. While generative models are increasingly us…
MAUPITI: On-Device Prototype-Based Learning on a Smart Infrared Sensor
Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso +7
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pix…
BlankSkip: Early-exit Object Detection onboard Nano-drones
Carlo Marra, Beatrice Alessandra Motetti, Alessio Burrello +3
Deploying tiny computer vision Deep Neural Networks (DNNs) on-board nano-sized drones is key for achieving autonomy, but is complicated by the extremely tight constraints of their…
Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data
Matteo Risso, Alessia Goffi, Beatrice Alessandra Motetti +6
Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most crit…
Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
Beatrice Alessandra Motetti, Matteo Risso, Alessio Burrello +3
The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mi…
Optimized Deployment of Deep Neural Networks for Visual Pose Estimation on Nano-drones
Matteo Risso, Francesco Daghero, Beatrice Alessandra Motetti +4
Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonethele…
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones
Beatrice Alessandra Motetti, Luca Crupi, Mustafa Omer Mohammed Elamin Elshaigi +4
Sub-10cm diameter nano-drones are gaining momentum thanks to their applicability in scenarios prevented to bigger flying drones, such as in narrow environments and close to humans.…
Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices
Alessio Burrello, Matteo Risso, Beatrice Alessandra Motetti +3
The rapid proliferation of computing domains relying on Internet of Things (IoT) devices has created a pressing need for efficient and accurate deep-learning (DL) models that can r…
PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization
Daniele Jahier Pagliari, Matteo Risso, Beatrice Alessandra Motetti +1
Accurate yet efficient Deep Neural Networks (DNNs) are in high demand, especially for applications that require their execution on constrained edge devices. Finding such DNNs in a…