Zobrazeno 1 - 10
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pro vyhledávání: '"Behboodi, A."'
Autor:
Silvestri, Gianluigi, Massoli, Fabio Valerio, Orekondy, Tribhuvanesh, Abdi, Afshin, Behboodi, Arash
A promising way to mitigate the expensive process of obtaining a high-dimensional signal is to acquire a limited number of low-dimensional measurements and solve an under-determined inverse problem by utilizing the structural prior about the signal.
Externí odkaz:
http://arxiv.org/abs/2407.07794
Compressed sensing combines the power of convex optimization techniques with a sparsity-inducing prior on the signal space to solve an underdetermined system of equations. For many problems, the sparsifying dictionary is not directly given, nor its e
Externí odkaz:
http://arxiv.org/abs/2407.06646
Modelling the propagation of electromagnetic signals is critical for designing modern communication systems. While there are precise simulators based on ray tracing, they do not lend themselves to solving inverse problems or the integration in an aut
Externí odkaz:
http://arxiv.org/abs/2406.14995
In recent years, solving optimization problems involving black-box simulators has become a point of focus for the machine learning community due to their ubiquity in science and engineering. The simulators describe a forward process $f_{\mathrm{sim}}
Externí odkaz:
http://arxiv.org/abs/2406.04261
Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general n
Externí odkaz:
http://arxiv.org/abs/2405.02140
Autor:
Arnold, Maximilian, Major, Bence, Massoli, Fabio Valerio, Soriaga, Joseph B., Behboodi, Arash
In the context of communication networks, digital twin technology provides a means to replicate the radio frequency (RF) propagation environment as well as the system behaviour, allowing for a way to optimize the performance of a deployed system base
Externí odkaz:
http://arxiv.org/abs/2401.17781
Autor:
Cesa, Gabriele, Behboodi, Arash
In this work, we introduce a novel approach based on algebraic topology to enhance graph convolution and attention modules by incorporating local topological properties of the data. To do so, we consider the framework of sheaf neural networks, which
Externí odkaz:
http://arxiv.org/abs/2311.10156
Lattice reduction is a combinatorial optimization problem aimed at finding the most orthogonal basis in a given lattice. In this work, we address lattice reduction via deep learning methods. We design a deep neural model outputting factorized unimodu
Externí odkaz:
http://arxiv.org/abs/2311.08170
Autor:
Hehn, Thomas M., Orekondy, Tribhuvanesh, Shental, Ori, Behboodi, Arash, Bucheli, Juan, Doshi, Akash, Namgoong, June, Yoo, Taesang, Sampath, Ashwin, Soriaga, Joseph B.
Estimating path loss for a transmitter-receiver location is key to many use-cases including network planning and handover. Machine learning has become a popular tool to predict wireless channel properties based on map data. In this work, we present a
Externí odkaz:
http://arxiv.org/abs/2310.04570
Autor:
Maryam Koochakzai, Zahra Behboodi Moghadam, Shahla Faal Siahkal, Hayedeh Arbabi, Elham Ebrahimi
Publikováno v:
Reproductive Health, Vol 21, Iss 1, Pp 1-10 (2024)
Abstract Introduction Suburban population is increasingly growing in Iran. People in the suburbs usually have limited sexual information and there are limited studies into their sexual issues. This study aims the effect of sexual education (SE) based
Externí odkaz:
https://doaj.org/article/e3a2314868364dd9b982ad7fee2f925b