Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Amir Zamir"'
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198359
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8a7d0dc7e00cbb1444d08e078f01491
https://doi.org/10.1007/978-3-031-19836-6_20
https://doi.org/10.1007/978-3-031-19836-6_20
Autor:
Payam Sharifan, Ali Jafarzadeh Esfehani, Amir Zamiri, Mansoureh Sadat Ekhteraee Toosi, Fatemeh Najar Sedgh Doust, Niloufar Taghizadeh, Maryam Mohammadi-Bajgiran, Hamideh Ghazizadeh, Fatemeh Khorram Rouz, Gordon Ferns, Majid Ghayour-Mobarhan
Publikováno v:
Journal of Health, Population and Nutrition, Vol 42, Iss 1, Pp 1-9 (2023)
Abstract Introduction Premenstrual syndrome (PMS) is a common condition that affects social and psychological well-being of women. The risk of PMS is higher among obese women. The aim of this study was to identify the factors that influence the sever
Externí odkaz:
https://doaj.org/article/737ec132687a420cb4c487142e311183
Publikováno v:
Avicenna Journal of Phytomedicine, Vol 1, Iss 2, Pp 74-77 (2011)
Objective: Cancer is a major health problem worldwide and current therapies for cancer are often limited by short-term efficacy due to drug resistance. There has been much interest in the use of naturally occurring compounds with chemo-preventive and
Externí odkaz:
https://doaj.org/article/595022f7fd174c1b986a48a1c7069526
Publikováno v:
Amir Zamir
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural networks. The primary distinction of the proposed transformations is that, un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5ee5f3800813da605e784000b904001
https://infoscience.epfl.ch/record/299218
https://infoscience.epfl.ch/record/299218
We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::300205c473abd28d907e1913942848be
https://infoscience.epfl.ch/record/294856
https://infoscience.epfl.ch/record/294856
Publikováno v:
In Biochemical Pharmacology 1994 47(5):910-913