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20122026
most citedGuess Who Rated This Movie: Identifying Users Through Subspace Clustering

45 citations · 168 across the 31 of their papers we have counts for

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9 papers · 1 filter

cs.NI2024

Distributed Experimental Design Networks

Yuanyuan Li, Lili Su, Carlee Joe-Wong +2

As edge computing capabilities increase, model learning deployments in diverse edge environments have emerged. In experimental design networks, introduced recently, network routing…

cs.NI2022

Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints

Yuezhou Liu, Yuanyuan Li, Lili Su +2

Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are n…

cs.NI2021

Rate Allocation and Content Placement in Cache Networks

Khashayar Kamran, Armin Moharrer, Stratis Ioannidis +1

We introduce the problem of optimal congestion control in cache networks, whereby \emph{both} rate allocations and content placements are optimized \emph{jointly}. We formulate thi…

cs.NI20201 cited

DeepFIR: Addressing the Wireless Channel Action in Physical-Layer Deep Learning

Francesco Restuccia, Salvatore D'Oro, Amani Al-Shawabka +3

Deep learning can be used to classify waveform characteristics (e.g., modulation) with accuracy levels that are hardly attainable with traditional techniques. Recent research has d…

cs.NI20205 cited

Hacking the Waveform: Generalized Wireless Adversarial Deep Learning

Francesco Restuccia, Salvatore D'Oro, Amani Al-Shawabka +4

This paper advances the state of the art by proposing the first comprehensive analysis and experimental evaluation of adversarial learning attacks to wireless deep learning systems…

cs.NI20198 cited

DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms

Francesco Restuccia, Salvatore D'Oro, Amani Al-Shawabka +5

Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy. By mapping inputs onto a very large feature space, deep learning algorithms can be traine…