Search Results for author: Marco Levorato

Found 21 papers, 9 papers with code

Dependable Distributed Training of Compressed Machine Learning Models

no code implementations22 Feb 2024 Francesco Malandrino, Giuseppe Di Giacomo, Marco Levorato, Carla Fabiana Chiasserini

The existing work on the distributed training of machine learning (ML) models has consistently overlooked the distribution of the achieved learning quality, focusing instead on its average value.

Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings

no code implementations14 Dec 2023 Seyed Amir Hossein Aqajari, Sina Labbaf, Phuc Hoang Tran, Brenda Nguyen, Milad Asgari Mehrabadi, Marco Levorato, Nikil Dutt, Amir M. Rahmani

We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.

SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing

no code implementations12 Oct 2023 Niloofar Bahadori, Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia

However, the size of the matrix grows with the number of antennas and subcarriers, resulting in an increasing amount of airtime overhead and computational load at the station.

Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systems

no code implementations22 Jun 2023 Juliano S. Assine, J. C. S. Santos Filho, Eduardo Valle, Marco Levorato

In approaches based on edge computing the execution of the models is offloaded to a compute-capable device positioned at the edge of 5G infrastructures.


Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings

no code implementations28 Apr 2023 Ali Tazarv, Sina Labbaf, Amir Rahmani, Nikil Dutt, Marco Levorato

Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models.

Active Learning reinforcement-learning

Matching DNN Compression and Cooperative Training with Resources and Data Availability

no code implementations2 Dec 2022 Francesco Malandrino, Giuseppe Di Giacomo, Armin Karamzade, Marco Levorato, Carla Fabiana Chiasserini

To make machine learning (ML) sustainable and apt to run on the diverse devices where relevant data is, it is essential to compress ML models as needed, while still meeting the required learning quality and time performance.

SC2 Benchmark: Supervised Compression for Split Computing

1 code implementation16 Mar 2022 Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attractive solution.

Data Compression Edge-computing +2

SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection

no code implementations11 Jan 2022 Davide Callegaro, Francesco Restuccia, Marco Levorato

We extensively evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.

Edge-computing object-detection +2

Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks

1 code implementation7 Dec 2021 Peyman Tehrani, Francesco Restuccia, Marco Levorato

Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles.

Autonomous Vehicles Federated Learning +2

Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks

no code implementations15 Nov 2021 Yoo Jeong Ha, Soyi Jung, Jae-Hyun Kim, Marco Levorato, Joongheon Kim

This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets.

Supervised Compression for Resource-Constrained Edge Computing Systems

2 code implementations21 Aug 2021 Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.

Data Compression Edge-computing +2

Personalized Stress Monitoring using Wearable Sensors in Everyday Settings

no code implementations31 Jul 2021 Ali Tazarv, Sina Labbaf, Stephanie M. Reich, Nikil Dutt, Amir M. Rahmani, Marco Levorato

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.

Heart Rate Variability Photoplethysmography (PPG)

A Deep Learning Approach to Predict Blood Pressure from PPG Signals

no code implementations30 Jul 2021 Ali Tazarv, Marco Levorato

Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal.

Photoplethysmography (PPG)

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

19 code implementations8 Mar 2021 Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.

Autonomous Vehicles Image Classification +4

Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems

2 code implementations20 Nov 2020 Yoshitomo Matsubara, Davide Callegaro, Sabur Baidya, Marco Levorato, Sameer Singh

In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.

Edge-computing Image Classification +2

Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems

no code implementations15 Sep 2020 Peyman Tehrani, Marco Levorato

The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems.

Fault Detection Multi-Task Learning +2

Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks

2 code implementations31 Jul 2020 Yoshitomo Matsubara, Marco Levorato

However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.

Edge-computing object-detection +1

Split Computing for Complex Object Detectors: Challenges and Preliminary Results

2 code implementations27 Jul 2020 Yoshitomo Matsubara, Marco Levorato

Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community.

Edge-computing Image Classification +1

Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems

2 code implementations1 Oct 2019 Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, Sameer Singh

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.

Edge-computing Image Classification +2

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