Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Michal Irani"'
Autor:
Oran Lang, Doron Yaya-Stupp, Ilana Traynis, Heather Cole-Lewis, Chloe R. Bennett, Courtney R. Lyles, Charles Lau, Michal Irani, Christopher Semturs, Dale R. Webster, Greg S. Corrado, Avinatan Hassidim, Yossi Matias, Yun Liu, Naama Hammel, Boris Babenko
Publikováno v:
EBioMedicine, Vol 102, Iss , Pp 105075- (2024)
Summary: Background: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors
Externí odkaz:
https://doaj.org/article/500f978dbec44cde8b3efb2076472b5c
Autor:
Guy Gaziv, Roman Beliy, Niv Granot, Assaf Hoogi, Francesca Strappini, Tal Golan, Michal Irani
Publikováno v:
NeuroImage, Vol 254, Iss , Pp 119121- (2022)
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We
Externí odkaz:
https://doaj.org/article/de04c565f2864f8f9116d8fbdf1c052b
Autor:
Shany Grossman, Guy Gaziv, Erin M. Yeagle, Michal Harel, Pierre Mégevand, David M. Groppe, Simon Khuvis, Jose L. Herrero, Michal Irani, Ashesh D. Mehta, Rafael Malach
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Deep convolutional neural networks (DCNNs) are able to identify faces on par with humans. Here, the authors record neuronal activity from higher visual areas in humans and show that face-selective responses in the brain show similarity to those in th
Externí odkaz:
https://doaj.org/article/e98fc9d58d49400dacc21b3515cf7cb8
Publikováno v:
Frontiers in Computational Neuroscience, Vol 12 (2018)
Visual perception involves continuously choosing the most prominent inputs while suppressing others. Neuroscientists induce visual competitions in various ways to study why and how the brain makes choices of what to perceive. Recently deep neural net
Externí odkaz:
https://doaj.org/article/400ec8d9a0dd4372950c3d22fe5021ad
Publikováno v:
Single Molecule Spectroscopy and Superresolution Imaging XVI.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c903c5d1de1d94804b6d3ee18ffc4ca5
https://doi.org/10.1117/12.2648282
https://doi.org/10.1117/12.2648282
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197895
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::046367d0d564e5346586d69350ca2bfa
https://doi.org/10.1007/978-3-031-19790-1_8
https://doi.org/10.1007/978-3-031-19790-1_8
Autor:
Ron Mokady, Omer Tov, Michal Yarom, Oran Lang, Inbar Mosseri, Tali Dekel, Daniel Cohen-Or, Michal Irani
StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited set of datasets, which are usually structurally aligned and well cura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9310d58eb5ed6de216c82b939994709a
Autor:
Michal Harel, Jose L. Herrero, Michal Irani, Ashesh D. Mehta, Guy Gaziv, Shany Grossman, Rafael Malach, Simon Khuvis, Pierre Mégevand, David M. Groppe, Erin M. Yeagle
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
Nature Communications
Nature Communications, Vol. 10, No 1 (2019) P. 4934
Nature Communications
Nature Communications, Vol. 10, No 1 (2019) P. 4934
The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::96e4263b2eed8e79105aea1ddea52ff8
http://link.springer.com/article/10.1038/s41467-019-12623-6
http://link.springer.com/article/10.1038/s41467-019-12623-6
Autor:
Michal Irani
Publikováno v:
Pattern Recognition Letters. 124:39-54
“Blind” visual inference can often be performed by exploiting the internal redundancy inside a single visual datum (whether an image or a video). The strong recurrence of patches inside a single image/video provides a powerful data-specific prior
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b57cd020278d785babba30867cc3381
https://doi.org/10.1016/j.patrec.2017.10.021
https://doi.org/10.1016/j.patrec.2017.10.021
Autor:
Guy Gaziv, Niv Granot, Francesca Strappini, Tal Golan, Roman Beliy, Michal Irani, Assaf Hoogi
Publikováno v:
bioRxiv
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c658bce20158ca070da14117a19006a
https://pubmed.ncbi.nlm.nih.gov/35342004
https://pubmed.ncbi.nlm.nih.gov/35342004