Mathematical text is too cumbersome to write because of the need to encode a tree structure in a left to right linear order. This paper defines two novel problems, namely structure prediction from unstructured representation and sequence prediction within a mathematical session, to help address mathematical text entry. The effectiveness of our approach relies on the fact that normal mathematica...

This paper presents methods to predict retrieval terms from relevant/surrounding words or descriptive texts in Japanese by using deep learning methods, which are implemented with stacked denoising autoencoders (SdA), as well as deep belief networks (DBN). To determine the effectiveness of using DBN and SdA for this task, we compare them with conventional machine learning methods, i.e., multi-la...

This article describes the influence of the kind of membership function on the accuracy of fuzzy logic forecasting model in the local power system. Fuzzy logic approach overcomes some problems related to practical implementations of traditional modelling and forecasting methods. This qualitative method of load forecasting can incorporate imprecise and ambiguous information in reasoning. The com...

In recent years, a wide range of markers have become available as potential tools to predict risk or progression of disease. In addition to such biological and genetic markers, short term outcome information may be useful in predicting long term disease outcomes. When such information is available, it would be desirable to combine this along with predictive markers to improve the prediction of ...

We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learnin...