Zobrazeno 1 - 10
of 366
pro vyhledávání: '"YILMAZ, Ozgur"'
We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case). It was recently shown that $\textit{O}(kdn\| \boldsymbol{\alpha}\|_{\infty}^{2})$ uniformly r
Externí odkaz:
http://arxiv.org/abs/2310.04984
Purpose: Convolutional neural networks can be trained to detect various conditions or patient traits based on retinal fundus photographs, some of which, such as the patient sex, are invisible to the expert human eye. Here we propose a methodology for
Externí odkaz:
http://arxiv.org/abs/2301.06675
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. There have been several works about BIHT but a theoretical understanding o
Externí odkaz:
http://arxiv.org/abs/2012.12886
Autor:
Arian, Arman, Yilmaz, Ozgur
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. When such a signal is acquired according to the principles of CS, the measurements still take on val
Externí odkaz:
http://arxiv.org/abs/1911.07525
Autor:
Arian, Arman, Yilmaz, Ozgur
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalized
Externí odkaz:
http://arxiv.org/abs/1911.07497
Autor:
Arian, Arman, Yilmaz, Ozgur
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting linear, non-adaptive measurements of a signal, which can be formalize
Externí odkaz:
http://arxiv.org/abs/1911.07428
Autor:
Melnykova, Kateryna, Yilmaz, Ozgur
Memoryless scalar quantization (MSQ) is a common technique to quantize frame coefficients of signals (which are used as a model for generalized linear samples), making them compatible with our digital technology. The process of quantization is genera
Externí odkaz:
http://arxiv.org/abs/1804.02839
In this paper we generalize the 1-bit matrix completion problem to higher order tensors. We prove that when $r=O(1)$ a bounded rank-$r$, order-$d$ tensor $T$ in $\mathbb{R}^{N} \times \mathbb{R}^{N} \times \cdots \times \mathbb{R}^{N}$ can be estimat
Externí odkaz:
http://arxiv.org/abs/1804.00108
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.