Cortical activations and novelty-promoting effects in reward-based learning


Cortical activations and novelty-promoting effects in reward-based learning – Recently, deep learning has been successfully applied to prediction of video content based on temporal and spatial information. In this work, we propose a novel framework, Deep Recurrent Neural Network (RNN), for video learning with attention based attention mechanisms. We propose a new algorithm (re)training convolutional recurrent unit (CRU) which can be used with the Recurrent Neural Network (RNN) to learn the relevant tasks from video images for the purpose of prediction of the relevance metrics. Furthermore, we propose a novel network architecture (CRU) which can utilize long-term memory to perform retrieval of video images and to predict the relevance score for the videos. Extensive experiments on RNN-RNN model have shown that our CRU achieves a substantial performance improvement when compared to both the RNE and the CRU. We conclude, that CRU can be used to learn a deep model to predict the videos’ relevance metrics better, and our CRU can be effectively adapted to a new state of the art video classification task.

We propose a novel approach to the optimization of the segmentation task of a large-scale dataset. As part of our research, we propose a novel method of estimating the shape of the segmentation task. This involves learning a neural model to predict the shape of a segmented image by applying different segmentation approaches according to an input model and a visual cue detector. The learned model can predict the shape accurately by learning a similarity metric and modeling the segmentation task as a multi-view sequential data. We further propose a novel multi-view sequence labeling framework. The proposed method is learned from different models on multiple datasets with different segmentation and visual cues detectors. The results of the evaluation of the proposed approach show that its performance compares well to the state-of-the-art methods while it is less accurate. Furthermore, we observed that our approach outperforms a deep framework in terms of classification accuracy.

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Cortical activations and novelty-promoting effects in reward-based learning

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  • Towards a theory of universal agents

    Toward Scalable Graph Convolutional Neural Network Clustering for Multi-Label Health PredictorsWe propose a novel approach to the optimization of the segmentation task of a large-scale dataset. As part of our research, we propose a novel method of estimating the shape of the segmentation task. This involves learning a neural model to predict the shape of a segmented image by applying different segmentation approaches according to an input model and a visual cue detector. The learned model can predict the shape accurately by learning a similarity metric and modeling the segmentation task as a multi-view sequential data. We further propose a novel multi-view sequence labeling framework. The proposed method is learned from different models on multiple datasets with different segmentation and visual cues detectors. The results of the evaluation of the proposed approach show that its performance compares well to the state-of-the-art methods while it is less accurate. Furthermore, we observed that our approach outperforms a deep framework in terms of classification accuracy.


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