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pro vyhledávání: '"Bonyadi, Mohammad Reza"'
Autor:
Bonyadi, Mohammad Reza
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 co
Externí odkaz:
http://arxiv.org/abs/2302.03124
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2022
Reinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, lead
Externí odkaz:
http://arxiv.org/abs/2004.05002
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. We also suggest the use of data fusion to further improve the seizure prediction accuracy. Data fusion in
Externí odkaz:
http://arxiv.org/abs/1806.08235
We introduce a novel approach for discriminative classification using evolutionary algorithms. We first propose an algorithm to optimize the total loss value using a modified 0-1 loss function in a one-dimensional space for classification. We then ex
Externí odkaz:
http://arxiv.org/abs/1804.09891
Autor:
Bonyadi, Mohammad Reza
In this paper we theoretically investigate underlying assumptions that have been used for designing adaptive particle swarm optimization algorithms in the past years. We relate these assumptions to the movement patterns of particles controlled by coe
Externí odkaz:
http://arxiv.org/abs/1802.04855
A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced. The algorithm seeks to find a transformation that best maps instances from the feature space to a space where they concentrate towards the center of their o
Externí odkaz:
http://arxiv.org/abs/1712.08259
Autor:
Truong, Nhan Duy, Nguyen, Anh Duy, Kuhlmann, Levin, Bonyadi, Mohammad Reza, Yang, Jiawei, Kavehei, Omid
Seizure prediction has attracted a growing attention as one of the most challenging predictive data analysis efforts in order to improve the life of patients living with drug-resistant epilepsy and tonic seizures. Many outstanding works have been rep
Externí odkaz:
http://arxiv.org/abs/1707.01976
In this paper we introduce a new classification algorithm called Optimization of Distributions Differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as
Externí odkaz:
http://arxiv.org/abs/1703.00989
Autor:
Truong, Nhan, Kuhlmann, Levin, Bonyadi, Mohammad Reza, Yang, Jiawei, Faulks, Andrew, Kavehei, Omid
Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if
Externí odkaz:
http://arxiv.org/abs/1701.08968
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. Most of these problems have been inspired by major industries so that solving them, b
Externí odkaz:
http://arxiv.org/abs/1606.06818