Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Bashiri, Mohammad"'
Autor:
Turishcheva, Polina, Fahey, Paul G., Vystrčilová, Michaela, Hansel, Laura, Froebe, Rachel, Ponder, Kayla, Qiu, Yongrong, Willeke, Konstantin F., Bashiri, Mohammad, Baikulov, Ruslan, Zhu, Yu, Ma, Lei, Yu, Shan, Huang, Tiejun, Li, Bryan M., De Wulf, Wolf, Kudryashova, Nina, Hennig, Matthias H., Rochefort, Nathalie L., Onken, Arno, Wang, Eric, Ding, Zhiwei, Tolias, Andreas S., Sinz, Fabian H., Ecker, Alexander S
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive mode
Externí odkaz:
http://arxiv.org/abs/2407.09100
Autor:
Turishcheva, Polina, Fahey, Paul G., Hansel, Laura, Froebe, Rachel, Ponder, Kayla, Vystrčilová, Michaela, Willeke, Konstantin F., Bashiri, Mohammad, Wang, Eric, Ding, Zhiwei, Tolias, Andreas S., Sinz, Fabian H., Ecker, Alexander S.
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding
Externí odkaz:
http://arxiv.org/abs/2305.19654
Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit and preserve the resulting uncertainty for downstream tasks. This study
Externí odkaz:
http://arxiv.org/abs/2210.11179
Autor:
Willeke, Konstantin F., Fahey, Paul G., Bashiri, Mohammad, Pede, Laura, Burg, Max F., Blessing, Christoph, Cadena, Santiago A., Ding, Zhiwei, Lurz, Konstantin-Klemens, Ponder, Kayla, Muhammad, Taliah, Patel, Saumil S., Ecker, Alexander S., Tolias, Andreas S., Sinz, Fabian H.
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety
Externí odkaz:
http://arxiv.org/abs/2206.08666
Publikováno v:
In Biomass and Bioenergy December 2024 191
Autor:
Fu, Jiakun, Pierzchlewicz, Paweł A., Willeke, Konstantin F., Bashiri, Mohammad, Muhammad, Taliah, Diamantaki, Maria, Froudarakis, Emmanouil, Restivo, Kelli, Ponder, Kayla, Denfield, George H., Sinz, Fabian, Tolias, Andreas S., Franke, Katrin
Publikováno v:
In Cell Reports 27 August 2024 43(8)
Publikováno v:
In Polymer 3 June 2024 304
Publikováno v:
In Journal of Photochemistry & Photobiology, A: Chemistry 1 June 2024 451
Autor:
Fathony, Rizal, Asif, Kaiser, Liu, Anqi, Bashiri, Mohammad Ali, Xing, Wei, Behpour, Sima, Zhang, Xinhua, Ziebart, Brian D.
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional labe
Externí odkaz:
http://arxiv.org/abs/1812.07526
Akademický článek
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