Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Mirzaei, Hossein"'
Autor:
Mirzaei, Hossein, Nafez, Mojtaba, Jafari, Mohammad, Soltani, Mohammad Bagher, Azizmalayeri, Mohammad, Habibi, Jafar, Sabokrou, Mohammad, Rohban, Mohammad Hossein
Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality, which can be interpreted as generalization across various distributions of training or test
Externí odkaz:
http://arxiv.org/abs/2408.10798
Autor:
Moakhar, Arshia Soltani, Azizmalayeri, Mohammad, Mirzaei, Hossein, Manzuri, Mohammad Taghi, Rohban, Mohammad Hossein
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains lim
Externí odkaz:
http://arxiv.org/abs/2310.09952
Autor:
Mirzaei, Hossein1 (AUTHOR), Iranpour, Abedin1 (AUTHOR), Esmaeilzadeh, Firooz2 (AUTHOR), Barouni, Mohsen3 (AUTHOR) mohsenbarooni@gmail.com, Mehrabi, Fatemeh1 (AUTHOR), Ranjbar, Ebrahim4 (AUTHOR), Sharifi, Hamid1,5 (AUTHOR) sharifihami@gmail.com
Publikováno v:
PLoS ONE. 9/6/2024, Vol. 19 Issue 9, p1-14. 14p.
Autor:
Mirzaei, Hossein, Salehi, Mohammadreza, Shahabi, Sajjad, Gavves, Efstratios, Snoek, Cees G. M., Sabokrou, Mohammad, Rohban, Mohammad Hossein
We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face a dramatic drop under the so-called "near-distribution" setting, where the differences between normal and anomalous samples are subtle. We fi
Externí odkaz:
http://arxiv.org/abs/2205.14297
Autor:
Salehi, Mohammadreza, Mirzaei, Hossein, Hendrycks, Dan, Li, Yixuan, Rohban, Mohammad Hossein, Sabokrou, Mohammad
Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliabi
Externí odkaz:
http://arxiv.org/abs/2110.14051
Autor:
Hedaiatnia, Sahar, Ariaeenejad, Shohreh, Kumleh, Hassan Hassani, Mirzaei, Hossein Hadavand, Shakeri, Fateh, Motamedi, Elaheh
Publikováno v:
In Bioresource Technology Reports February 2024 25
Autor:
Mirzaei, Hossein1 (AUTHOR), Eybpoosh, Sana2 (AUTHOR), Mehrabi, Fatemeh1 (AUTHOR), Shojaei, Mohammad Reza3 (AUTHOR), Mirzazadeh, Ali1,4 (AUTHOR), Khezri, Mehrdad1,5 (AUTHOR), Nasiri, Naser6 (AUTHOR), Sharifi, Hamid1,7 (AUTHOR) hsharifi@kmu.ac.ir
Publikováno v:
BMC Infectious Diseases. 1/2/2024, Vol. 24 Issue 1, p1-13. 13p.
Autor:
Vahdat, Mansoureh, Allahqoli, Leila, Mirzaei, Hossein, Giovannucci, Edward, Salehiniya, Hamid, Mansouri, Ghazal, Alkatout, Ibrahim
Publikováno v:
In Complementary Therapies in Clinical Practice February 2022 46
Autor:
Mehrabi, Fatemeh, Mehmandoost, Soheil, Mirzazadeh, Ali, Noroozi, Alireza, Tavakoli, Fatemeh, Mirzaei, Hossein, Khezri, Mehrdad, Mousavian, Ghazal, Ghalekhani, Nima, Afsar Kazerooni, Parvin, Navaiian, Fatemeh, Farajzadeh, Zahra, Shokoohi, Mostafa, Sharifi, Hamid, Karamouzian, Mohammad
Publikováno v:
International Journal of Mental Health & Addiction; Aug2024, Vol. 22 Issue 4, p2378-2390, 13p
Autor:
Abedipour, Fatemah, Mirzaei, Hossein Hadavand, Ansari, Hossein, Ehsanzadeh, Neda, Rashki, Amin, Vahedi, Mohammad Mahdi, Rashki, Asma
Publikováno v:
Drug Research; Jul2024, Vol. 74 Issue 6, p290-295, 6p