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Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts...
In this work, we conduct a systematic exploration on the promise and challenges of deep learning for the sparse matrix format selection. We propose a set of novel techniques to solve special challenges to deep learning, including input matrix representations, a late-merging deep neural network structure design, and the use of transfer learning to alleviate cross-architecture portability issues.
Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this...
Churn prediction is very important to the insurance industry. Therefore, there is a big value to investigate how to improve its performance. More importantly, a good model can be used by a common service provider and benefit many companies. State-of-the-art methods either use 1) shallow models such as logistic regression, with sophisticated feature engineering, or 2) deep models that learn features...
This work uses deep learning methods for intraday directional movements prediction of Standard & Poor's 500 index using financial news titles and a set of technical indicators as input. Deep learning methods can detect and analyze complex patterns and interactions in the data automatically allowing speed up the trading process. This paper focus on architectures such as Convolutional Neural Networks...
In this paper, we propose a deep convolutional neural network model for in-bed behavior recognition and bed-exit prediction. This model extracts features for training from depth images taken by depth cameras in two categories: in-bed images taken several time intervals before a patient gets out of bed, and usual in-bed activity images. The depth camera-based model features grayscale and low-resolution...
Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages...
Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on...
Opinion mining and sentiment analysis has recently become a hot topic in the field of natural language processing and text mining. This paper addresses the problem of overall rating for comments in Vietnamese language. The traditional approach of using bag-of-words for feature representation would cause a very high dimensional feature space and doesn't reflect relationship between words. To capture...
Determining the optimal design of a watershed is a highly subjective process which involves the consideration of many distinct factors by several different stakeholder groups. We describe additional functionality for our watershed planning system, called WRESTORE (Watershed REstoration Using Spatio-Temporal Optimization of REsources) (http://wrestore.iupui.edu), where stakeholders can collaboratively...
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