Adversarial Learning for Brain-Computer Interfacing: A Survey


Adversarial Learning for Brain-Computer Interfacing: A Survey – We present a framework for training deep convolutional neural networks to predict action videos with a single feed of video video data. Our model has been evaluated on a wide variety of action videos captured during the last months. In particular, we evaluate the predictive performance of models trained in the context of the task of predicting action sequences. We demonstrate that deep neural networks trained with the CNN architecture are better at predicting a particular action than those trained without CNNs, and therefore, CNNs can be very useful for this task. We will provide a framework for further investigation related to the task of video prediction.

In this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.

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Adversarial Learning for Brain-Computer Interfacing: A Survey

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  • Learning with a Novelty-Assisted Learning Agent

    Pervasive Sparsity Modeling for Compressed Image AcquisitionIn this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.


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