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of 419
pro vyhledávání: '"Yilmaz Özgür"'
Autor:
López, Oscar, Yılmaz, Özgür
Many empirical studies suggest that samples of continuous-time signals taken at locations randomly deviated from an equispaced grid (i.e., off-the-grid) can benefit signal acquisition, e.g., undersampling and anti-aliasing. However, explicit statemen
Externí odkaz:
http://arxiv.org/abs/2211.02719
Deep learning has seen tremendous interest in medical imaging, particularly in the use of convolutional neural networks (CNNs) for developing automated diagnostic tools. The facility of its non-invasive acquisition makes retinal fundus imaging amenab
Externí odkaz:
http://arxiv.org/abs/2207.09624
In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN). The problem of compres
Externí odkaz:
http://arxiv.org/abs/2207.09340
In school choice problems, the motivation for students' welfare (efficiency) is restrained by concerns to respect schools' priorities (fairness). Among the fair matchings, even the best one in terms of welfare (SOSM) is inefficient. Moreover, any mec
Externí odkaz:
http://arxiv.org/abs/2205.00032
Generalized compressed sensing (GCS) is a paradigm in which a structured high-dimensional signal may be recovered from random, under-determined, and corrupted linear measurements. Generalized Lasso (GL) programs are effective for solving GCS problems
Externí odkaz:
http://arxiv.org/abs/2010.08884
Akademický článek
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Random linear mappings are widely used in modern signal processing, compressed sensing and machine learning. These mappings may be used to embed the data into a significantly lower dimension while at the same time preserving useful information. This
Externí odkaz:
http://arxiv.org/abs/2001.10631
Autor:
Köroğlu, Muhammed, Özdeş, Hüseyin Utku, Özbey, Rafet, Yılmaz, Özgür, Ergen, Emre, Oklu, Yunus, Aslantürk, Okan
Publikováno v:
In Foot and Ankle Surgery August 2023 29(6):462-465
Autor:
Yilmaz, Özgür
Publikováno v:
Karadeniz Araştırmaları Enstitüsü Dergisi / The Journal of Institute of Black Sea Studies. 9(18):165-190
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=1136023
The use of generalized LASSO is a common technique for recovery of structured high-dimensional signals. Each generalized LASSO program has a governing parameter whose optimal value depends on properties of the data. At this optimal value, compressed
Externí odkaz:
http://arxiv.org/abs/1810.11968