Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Noshad, Morteza"'
Autor:
Khiarak, Jalil Nourmohammadi, Ahmadi, Ammar, Saeed, Taher Ak-bari, Asgari-Chenaghlu, Meysam, Atabay, Toğrul, Karimi, Mohammad Reza Baghban, Ceferli, Ismail, Hasanvand, Farzad, Mousavi, Seyed Mahboub, Noshad, Morteza
This paper introduces a pioneering English-Azerbaijani (Arabic Script) parallel corpus, designed to bridge the technological gap in language learning and machine translation (MT) for under-resourced languages. Consisting of 548,000 parallel sentences
Externí odkaz:
http://arxiv.org/abs/2407.05189
Autor:
Khiarak, Jalil Nourmohammadi, Nasab, Samaneh Salehi, Jaryani, Farhang, Moafinejad, Seyed Naeim, Pourmohamad, Rana, Amini, Yasin, Noshad, Morteza
Iris segmentation and localization in unconstrained environments is challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. To address this problem, we present a U-Net with a pre-trained MobileNetV2
Externí odkaz:
http://arxiv.org/abs/2112.05236
The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems. We address the problem of predicting clinical workups for specialty
Externí odkaz:
http://arxiv.org/abs/2007.12161
We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-opt
Externí odkaz:
http://arxiv.org/abs/1909.07192
Autor:
Fouladvand, Sajjad, Gomez, Federico Reyes, Nilforoshan, Hamed, Schwede, Matthew, Noshad, Morteza, Jee, Olivia, You, Jiaxuan, Sosic, Rok, Leskovec, Jure, Chen, Jonathan
Publikováno v:
In Journal of Biomedical Informatics July 2023 143
Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information theory. Many bounds on the Bayes binary classification error rate depend
Externí odkaz:
http://arxiv.org/abs/1810.01015
The Mutual Information (MI) is an often used measure of dependency between two random variables utilized in information theory, statistics and machine learning. Recently several MI estimators have been proposed that can achieve parametric MSE converg
Externí odkaz:
http://arxiv.org/abs/1801.09125
Autor:
Noshad, Morteza, Hero III, Alfred O.
We propose a scalable divergence estimation method based on hashing. Consider two continuous random variables $X$ and $Y$ whose densities have bounded support. We consider a particular locality sensitive random hashing, and consider the ratio of samp
Externí odkaz:
http://arxiv.org/abs/1801.00398
Publikováno v:
In Information Theory (ISIT), 2017 IEEE International Symposium on (pp. 903-907). IEEE
We propose a direct estimation method for R\'{e}nyi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where $\eta:=M/N$ is a consta
Externí odkaz:
http://arxiv.org/abs/1702.05222
Publikováno v:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6095-6099, Mar. 2017
Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov fac
Externí odkaz:
http://arxiv.org/abs/1609.03912