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Chenxi Liu, Israel Cohen, Rotem Vishinkin and Hossam Haick
Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid and cost-effective testing for the real-time detection of TB...
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Alon Ascoli, Martin Weiher, Melanie Herzig, Stefan Slesazeck, Thomas Mikolajick and Ronald Tetzlaff
This manuscript provides a comprehensive tutorial on the operating principles of a bio-inspired Cellular Nonlinear Network, leveraging the local activity of NbOx
x
memristors to apply a spike-based computing paradigm, which is expected to deliver such a...
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Carlos Lassance, Vincent Gripon and Antonio Ortega
Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs...
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Rasoul Shafipour and Gonzalo Mateos
We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. The setup enta...
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Mario Coutino, Sundeep Prabhakar Chepuri, Takanori Maehara and Geert Leus
To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a differe...
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Tiffany Fan, David I. Shuman, Shashanka Ubaru and Yousef Saad
We propose and investigate two new methods to approximate ??(??)??
f
(
A
)
b
for large, sparse, Hermitian matrices ??
A
. Computations of this form play an important role in numerous signal processing and machine learning tasks. The main idea behind bot...
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