Music generation with variational recurrent autoencoder supported by history

A new artificial neural network architecture that helps generating longer melodic patterns is introduced alongside with methods for post-generation filtering. The proposed approach, called variational autoencoder supported by history, is based on a recurrent highway gated network combined with a variational autoencoder. The combination of this architecture with filtering heuristics allows the generation of pseudo-live, acoustically pleasing, melodically diverse music.

Semi-Markov Reinforcement Learning for Stochastic Resource Collection

We show that the task of collecting stochastic, spatially distributed resources (Stochastic Resource Collection, SRC) may be considered as a Semi-Markov-Decision-Process. Our Deep-Q-Network (DQN) based approach uses a novel scalable and transferable artificial neural network architecture. The concrete use-case of the SRC is an officer (single agent) trying to maximize the amount of fined parking violations in his area. We evaluate our approach on a environment based on the real-world parking data of the city of Melbourne. In small, hence simple, settings with short distances between resources and few simultaneous violations, our approach is comparable to previous work. When the size of the network grows (and hence the amount of resources) our solution significantly outperforms preceding methods. Moreover, applying a trained agent to a non-overlapping new area outperforms existing approaches.

Assessment of the Average Level of TOEFL Score by using SOM (Self Organizing Map) and K-Mean Clustering Techniques

Economic growth as measured by GDP growth rates and economic growth set as an increase in GDP strongly helps government predictions about the economic situation and the formation of economic development strategies. This measurement is done by combining mathematical and computer technology to make qualitative and quantitative predictions scientifically and appropriately for economic growth trends. It is a good practical sense to use scientific and proven methods to predict future GDP development trends of a particular economy. In some cases, machine learning methods have proven to be better forecasting results than statistical methods. A Deep Neural Network (DNN) is one type of ANN (Artificial Neural network) architecture based on deep MLP (Multi Layer Perceptron), which uses Deep Learning training techniques. This study proposes the use of DNN to predict the percentage of GDP distribution at current prices by industry sector. In this case, the DNN used will have multiple outputs as many industry sectors. The aim of this study is how to predict for the next period with the smallest possible prediction errors by using DNN.