1 code implementation • 6 Feb 2023 • Mohammad Reza Bonyadi
We introduce Autodecompose, a novel self-supervised generative model that decomposes data into two semantically independent properties: the desired property, which captures a specific aspect of the data (e. g. the voice in an audio signal), and the context property, which aggregates all other information (e. g. the content of the audio signal), without any labels given.
1 code implementation • 10 Apr 2020 • Mohammad Reza Bonyadi, Rui Wang, Maryam Ziaei
We prove that, under certain assumptions and regardless of the reinforcement learning algorithm used, these two strategies maintain the order of policies in the space of all possible policies in terms of their total reward, and, by extension, maintain the optimal policy.
no code implementations • 20 Jun 2018 • Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Omid Kavehei
In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible.
1 code implementation • 26 Apr 2018 • Mohammad Reza Bonyadi, David C. Reutens
We then extend this algorithm for multi-dimensional classification using an evolutionary algorithm.
no code implementations • 13 Feb 2018 • Mohammad Reza Bonyadi
We relate these assumptions to the movement patterns of particles controlled by coefficient values (inertia weight and acceleration coefficient) and introduce three factors, namely the autocorrelation of the particle positions, the average movement distance of the particle in each iteration, and the focus of the search, that describe these movement patterns.
no code implementations • 22 Dec 2017 • Mohammad Reza Bonyadi, Viktor Vegh, David C. Reutens
A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced.
no code implementations • 6 Jul 2017 • Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Omid Kavehei
The proposed approach achieves sensitivity of 81. 4%, 81. 2%, 82. 3% and false prediction rate (FPR) of 0. 06/h, 0. 16/h, 0. 22/h on Freiburg Hospital intracranial EEG (iEEG) dataset, Children's Hospital of Boston-MIT scalp EEG (sEEG) dataset, and Kaggle American Epilepsy Society Seizure Prediction Challenge's dataset, respectively.
no code implementations • 2 Mar 2017 • Mohammad Reza Bonyadi, Quang M. Tieng, David C. Reutens
Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods.
no code implementations • 31 Jan 2017 • Nhan Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Andrew Faulks, Omid Kavehei
We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy.
no code implementations • 22 Jun 2016 • Mohammad Reza Bonyadi, Zbigniew Michalewicz, Frank Neumann, Markus Wagner
Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems.