1 code implementation • 16 May 2023 • Alireza Zaeemzadeh, Giulio Tononi
We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations.
no code implementations • 30 Dec 2022 • Larissa Albantakis, Leonardo Barbosa, Graham Findlay, Matteo Grasso, Andrew M Haun, William Marshall, William GP Mayner, Alireza Zaeemzadeh, Melanie Boly, Bjørn E Juel, Shuntaro Sasai, Keiko Fujii, Isaac David, Jeremiah Hendren, Jonathan P Lang, Giulio Tononi
IIT aims to account for the properties of experience in physical (operational) terms.
no code implementations • 30 Dec 2022 • William Marshall, Matteo Grasso, William GP Mayner, Alireza Zaeemzadeh, Leonardo S Barbosa, Erick Chastain, Graham Findlay, Shuntaro Sasai, Larissa Albantakis, Giulio Tononi
Integrated information theory (IIT) starts from consciousness itself and identifies a set of properties (axioms) that are true of every conceivable experience.
1 code implementation • 2 Jul 2021 • Nazmul Karim, Alireza Zaeemzadeh, Nazanin Rahnavard
The proposed scheme, referred to as RL-NCS, aims to boost the performance of signal recovery through an optimal and adaptive distribution of sensing energy among two groups of coefficients of the signal, referred to as the region of interest (ROI) coefficients and non-ROI coefficients.
2 code implementations • CVPR 2021 • Alireza Zaeemzadeh, Niccolo Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, Mubarak Shah
In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces.
no code implementations • ICCV 2021 • Alireza Zaeemzadeh, Shabnam Ghadar, Baldo Faieta, Zhe Lin, Nazanin Rahnavard, Mubarak Shah, Ratheesh Kalarot
For example, a user can ask for retrieving images similar to a query image, but with a different hair color, and no preference for absence/presence of eyeglasses in the results.
2 code implementations • CVPR 2019 • Mohsen Joneidi, Alireza Zaeemzadeh, Nazanin Rahnavard, Mubarak Shah
In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples.
1 code implementation • 18 May 2018 • Alireza Zaeemzadeh, Nazanin Rahnavard, Mubarak Shah
We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective.