Zobrazeno 1 - 10
of 26
pro vyhledávání: '"OKTAR, Yigit"'
Autor:
Oktar, Yigit
By considering a discrete tape where each cell corresponds to an integer, thus to a possible sum, a pseudo-polynomial solution can be given to subset sum problem, which is an NP-complete problem and a cornerstone application for this study, using shi
Externí odkaz:
http://arxiv.org/abs/2401.02420
Casting graph isomorphism as a point set registration problem using a simplex embedding and sampling
Autor:
Oktar, Yigit
Graph isomorphism is an important problem as its worst-case time complexity is not yet fully understood. In this study, we try to draw parallels between a related optimization problem called point set registration. A graph can be represented as a poi
Externí odkaz:
http://arxiv.org/abs/2111.09696
Autor:
Oktar, Yigit, Turkan, Mehmet
In conventional machine learning applications, each data attribute is assumed to be orthogonal to others. Namely, every pair of dimension is orthogonal to each other and thus there is no distinction of in-between relations of dimensions. However, thi
Externí odkaz:
http://arxiv.org/abs/2006.08321
Autor:
Oktar, Yigit, Turkan, Mehmet
Dictionary learning for sparse representations has been successful in many reconstruction tasks. Simplicial learning is an adaptation of dictionary learning, where subspaces become clipped and acquire arbitrary offsets, taking the form of simplices.
Externí odkaz:
http://arxiv.org/abs/2005.07076
In a standard Turing test, a machine has to prove its humanness to the judges. By successfully imitating a thinking entity such as a human, this machine then proves that it can also think. Some objections claim that Turing test is not a tool to demon
Externí odkaz:
http://arxiv.org/abs/2002.02334
It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual
Externí odkaz:
http://arxiv.org/abs/1912.10201
Autor:
Oktar, Yigit, Turkan, Mehmet
In conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to ra
Externí odkaz:
http://arxiv.org/abs/1701.04018
Autor:
Oktar, Yigit
Monocular depth estimation is an interesting and challenging problem as there is no analytic mapping known between an intensity image and its depth map. Recently there has been a lot of data accumulated through depth-sensing cameras, in parallel to t
Externí odkaz:
http://arxiv.org/abs/1606.08315
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