Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Monsefi, Reza"'
Publikováno v:
in IEEE Signal Processing Letters, vol. 28, pp. 713-717, 2021
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated from the so-called proposal distribution, and the choice of this prop
Externí odkaz:
http://arxiv.org/abs/2209.13716
Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant
Externí odkaz:
http://arxiv.org/abs/2201.00227
Autor:
Godaz, Reza, Ghojogh, Benyamin, Hosseini, Reshad, Monsefi, Reza, Karray, Fakhri, Crowley, Mark
Publikováno v:
Proceedings of The 13th Asian Conference on Machine Learning, PMLR, vol. 157, pp. 1-16, 2021
This work concentrates on optimization on Riemannian manifolds. The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm is a commonly used quasi-Newton method for numerical optimization in Euclidean spaces. Riemannian LBFGS (RLBFGS) is
Externí odkaz:
http://arxiv.org/abs/2108.11019
Metric learning algorithms aim to learn a distance function that brings the semantically similar data items together and keeps dissimilar ones at a distance. The traditional Mahalanobis distance learning is equivalent to find a linear projection. In
Externí odkaz:
http://arxiv.org/abs/2106.06420
In this paper, we tackle two important problems in low-rank learning, which are partial singular value decomposition and numerical rank estimation of huge matrices. By using the concepts of Krylov subspaces such as Golub-Kahan bidiagonalization (GK-b
Externí odkaz:
http://arxiv.org/abs/2104.10785
Multiple Kernel Learning is a conventional way to learn the kernel function in kernel-based methods. MKL algorithms enhance the performance of kernel methods. However, these methods have a lower complexity compared to deep learning models and are inf
Externí odkaz:
http://arxiv.org/abs/2102.13337
Publikováno v:
In Applied Soft Computing November 2023 147
Publikováno v:
In Information Sciences November 2023 648
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this
Externí odkaz:
http://arxiv.org/abs/1810.11071
This paper proposes a novel method for tamper detection and recovery using semi-fragile data hiding, based on Lifting Wavelet Transform (LWT) and Feed-Forward Neural Network (FNN). In TRLF, first, the host image is decomposed up to one level using LW
Externí odkaz:
http://arxiv.org/abs/1802.07119