Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Finamore, Alessandro"'
Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, healthcare records, or stock hi
Externí odkaz:
http://arxiv.org/abs/2406.15327
Diffusion models for Text-to-Image (T2I) conditional generation have seen tremendous success recently. Despite their success, accurately capturing user intentions with these models still requires a laborious trial and error process. This challenge is
Externí odkaz:
http://arxiv.org/abs/2405.20759
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction i
Externí odkaz:
http://arxiv.org/abs/2401.10754
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC)
Externí odkaz:
http://arxiv.org/abs/2310.13935
Autor:
Finamore, Alessandro, Wang, Chao, Krolikowski, Jonatan, Navarro, Jose M., Chen, Fuxing, Rossi, Dario
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference c
Externí odkaz:
http://arxiv.org/abs/2309.09733
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 Traffic Classification (TC). However, to tame the dependency from task-speci
Externí odkaz:
http://arxiv.org/abs/2305.12432
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 being learned together while others may be detrimental to one another.
Externí odkaz:
http://arxiv.org/abs/2301.02873
Autor:
Franzese, Giulio, Rossi, Simone, Yang, Lixuan, Finamore, Alessandro, Rossi, Dario, Filippone, Maurizio, Michiardi, Pietro
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 started to lay down a theoretical foundation for these models, an analyti
Externí odkaz:
http://arxiv.org/abs/2206.05173
While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with respect to real-time traffic measurements requirements. To reduce the DL i
Externí odkaz:
http://arxiv.org/abs/2112.06671
Autor:
Bovenzi, Giampaolo, Yang, Lixuan, Finamore, Alessandro, Aceto, Giuseppe, Ciuonzo, Domenico, Pescapè, Antonio, Rossi, Dario
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 to identify Internet applications' traffic. Even with the aid of hardwa
Externí odkaz:
http://arxiv.org/abs/2107.04464