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20202023
most citedLocal Evaluation of Time Series Anomaly Detection Algorithms

85 citations · 177 across the 17 of their papers we have counts for

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8 papers · 1 filter

cs.LG2023

Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification

Idio Guarino, Chao Wang, Alessandro Finamore +2

The popularity of Deep Learning (DL), coupled with network traffic visibility reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited interest towards Traffi…

cs.LG2023★ 4 cited

Continuous-Time Functional Diffusion Processes

Giulio Franzese, Giulio Corallo, Simone Rossi +3

We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework…

cs.LG2023

"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning

Raphael Azorin, Massimo Gallo, Alessandro Finamore +2

While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from…

cs.LG2022★ 85 cited

Local Evaluation of Time Series Anomaly Detection Algorithms

Alexis Huet, Jose Manuel Navarro, Dario Rossi

In recent years, specific evaluation metrics for time series anomaly detection algorithms have been developed to handle the limitations of the classical precision and recall. Howev…

cs.LG2022

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).…

cs.LG2021★ 3 cited

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…