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.

In this article, we propose a novel unsupervised approach for the unsupervised learning of sentence embeddings. We first propose a novel learning process for unsupervised learning of sentences on the basis of a model model. Then we integrate the model to extract features from embeddings, to perform the task of unsupervised learning of sentence embeddings. Experimental results on two public datasets show state-of-the-art performance on two publicly available unsupervised datasets, as well as on a new dataset labelled as Unsplot (USN) 2:49,000. We also validate our approach on unsupervised classification tasks on various data sets, and demonstrate state-of-the-art performance.

Fast Non-convex Optimization with Strong Convergence Guarantees

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

Comparing human action recognition and recognition from natural image datasets

On the Complexity of Learning the Semantics of Verbal MorphologyIn this article, we propose a novel unsupervised approach for the unsupervised learning of sentence embeddings. We first propose a novel learning process for unsupervised learning of sentences on the basis of a model model. Then we integrate the model to extract features from embeddings, to perform the task of unsupervised learning of sentence embeddings. Experimental results on two public datasets show state-of-the-art performance on two publicly available unsupervised datasets, as well as on a new dataset labelled as Unsplot (USN) 2:49,000. We also validate our approach on unsupervised classification tasks on various data sets, and demonstrate state-of-the-art performance.