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Found 10 papers, 3 papers with code
Autodecompose: A generative self-supervised model for semantic decomposition
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.
Self Punishment and Reward Backfill for Deep Q-Learning
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.
Semi-supervised Seizure Prediction with Generative Adversarial Networks
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.
Optimal-margin evolutionary classifier
We then extend this algorithm for multi-dimensional classification using an evolutionary algorithm.
A theoretical guideline for designing an effective adaptive particle swarm
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.
Linear centralization classifier
A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced.
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis
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.
Optimization of distributions differences for classification
Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods.
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection
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.
Evolutionary computation for multicomponent problems: opportunities and future directions
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.
