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.

The task of learning a Bayesian decision-making process is to estimate the optimal decision-making policy if there exists a sufficiently large subset of variables. If there are at least some sufficiently large variables, then one can use the Bayesian inference technique to find a good policy in a large sample of variables. However, the estimation of the decision-making policy in any given problem has a considerable risk of being suboptimal since the uncertainty in the parameters of the problem poses a significant problem. In the recent years, learning-based Bayes methods have been considered for such problems. In this paper we present an algorithm and an algorithm for Bayes prediction for continuous, non-linear domains. The algorithm is a Bayesian inference (FIB) method and thus requires the estimation of the Bayesian policy via the use of the Bayesian inference technique. Our algorithm learns the optimal policy based on the estimation of the Bayesian policy. Experimental results show that our algorithm outperforms competing Bayesian inference algorithms.

Deep Multi-view Feature Learning for Text Recognition

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

Machine learning and networked sensing

Bayesian Inference in Markov Decision Processes with Bayes for exampleThe task of learning a Bayesian decision-making process is to estimate the optimal decision-making policy if there exists a sufficiently large subset of variables. If there are at least some sufficiently large variables, then one can use the Bayesian inference technique to find a good policy in a large sample of variables. However, the estimation of the decision-making policy in any given problem has a considerable risk of being suboptimal since the uncertainty in the parameters of the problem poses a significant problem. In the recent years, learning-based Bayes methods have been considered for such problems. In this paper we present an algorithm and an algorithm for Bayes prediction for continuous, non-linear domains. The algorithm is a Bayesian inference (FIB) method and thus requires the estimation of the Bayesian policy via the use of the Bayesian inference technique. Our algorithm learns the optimal policy based on the estimation of the Bayesian policy. Experimental results show that our algorithm outperforms competing Bayesian inference algorithms.