Ontology-Based Topic Labeling and Quality Prediction
AbstractProbabilistic topic models based on Latent Dirichlet Allocation (LDA) are increasingly used to discover hidden structure behind big text corpora. Although topic models are extremely useful...
View ArticleBuilding FP-Tree on the Fly: Single-Pass Frequent Itemset Mining
AbstractThe FP-Growth algorithm has been studied extensively in the field of frequent pattern mining. The algorithm offers the advantage of avoiding costly database scans in comparison with...
View ArticleComputational Role of Astrocytes in Bayesian Inference and Probability...
AbstractThe past few years have seen new research methods confirming more confidently that glia have a key information processing role in the brain, specifically in relation to learning capability....
View ArticleGeodesic and contour optimization using conformal mapping
AbstractWe propose a novel optimization algorithm for differentiable functions utilizing geodesics and contours under conformal mapping. The algorithm can locate multiple optima by first following a...
View ArticleMemory-adaptive high utility sequential pattern mining over data streams
AbstractHigh utility sequential pattern (HUSP) mining has emerged as an important topic in data mining. A number of studies have been conducted on mining HUSPs, but they are mainly intended for...
View ArticleContrast Pattern Based Collaborative Behavior Recommendation for Life...
AbstractPositive attitudes and happiness have major impacts on human health and in particular recovery from illness. While contributing factors leading human beings to positive emotional states are...
View ArticleMining significant high utility gene regulation sequential patterns
AbstractBackgroundMining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount...
View ArticleUsing Neural Network for Identifying Clickbaits in Online News Media
AbstractOnline news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called clickbaits....
View ArticleDynamic Joint Variational Graph Autoencoders
AbstractLearning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world...
View ArticleQuestion-Worthy Sentence Selection for Question Generation
Abstract The problem of automatic question generation from text is of increasing importance due to many useful applications. While deep neural networks achieved success in generating questions from...
View ArticleQuantification of Shape Properties and Their Effects on Particle Packing of...
AbstractParticle shape is known for its significant influence on the engineering properties of coarse grain soil. But it remains difficult to quantify how the different scales of shape properties will...
View ArticleA Novel Approach to Determining the Quality of News Headlines
AbstractHeadlines play a pivotal role in engaging and attracting news readers since headlines are the most visible parts of the news articles, especially in online media. Due to this importance, news...
View ArticleAdaptive Momentum Coefficient for Neural Network Optimization
AbstractWe propose a novel and efficient momentum-based first-order algorithm for optimizing neural networks which uses an adaptive coefficient for the momentum term. Our algorithm, called Adaptive...
View ArticleZipLine: an optimized algorithm for the elastic bulk synchronous parallel model
AbstractThe bulk synchronous parallel (BSP) is a celebrated synchronization model for general-purpose parallel computing that has successfully been employed for distributed training of deep learning...
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