18 citations · 52 across the 7 of their papers we have counts for
7 papers
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models
Giulio Franzese, Simone Rossi, Lixuan Yang +4
Score-based diffusion models are a class of generative models whose dynamics is described by stochastic differential equations that map noise into data. While recent works have sta…
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
Lixuan Yang, Dario Rossi
Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particul…
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Kevin Fauvel, Fuxing Chen, Dario Rossi
Traffic classification, i.e. the identification of the type of applications flowing in a network, is a strategic task for numerous activities (e.g., intrusion detection, routing).…
Thinkback: Task-SpecificOut-of-Distribution Detection
Lixuan Yang, Dario Rossi
The increased success of Deep Learning (DL) has recently sparked large-scale deployment of DL models in many diverse industry segments. Yet, a crucial weakness of supervised model…
A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
Giampaolo Bovenzi, Lixuan Yang, Alessandro Finamore +4
The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers t…
Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications
Lixuan Yang, Alessandro Finamore, Feng Jun +1
The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While classification of known traffic is a w…
Heterogeneous Data-Aware Federated Learning
Lixuan Yang, Cedric Beliard, Dario Rossi
Federated learning (FL) is an appealing concept to perform distributed training of Neural Networks (NN) while keeping data private. With the industrialization of the FL framework,…