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Recurrent neural networks (RNNs) recently received considerable attention for sequence modeling and time series analysis. Many time series contain periods, e.g. seasonal changes in weather time series or electricity usage at day and night time. Here, we first analyze the behavior of RNNs with an attention mechanism with respect to periods in time series and illustrate that they fail to model periods...
We propose in this paper a new, alternative approach for the problem of finding a set of representative objects in large datasets. To do so, we first formulate the general Instance Selection Problem (ISP) and then study three variants of that in order to select instances from different regions of the data. These variants aim at finding the objects located in three very different locations of the data:...
We address in this paper the problem of efficiently finding a few number of representative frequent itemsets in transaction matrices. To do so, we propose to rely on matrix decomposition techniques, and more precisely on Constrained Binary Matrix Factorization (CBMF) which decomposes a given binary matrix into the product of two lower dimensional binary matrices, called factors. We first show, under...
Identification of social relationships in social networks, especially cell phone networks, has attracted a lot of attention recently. Mobile phones continue to be one of the main methods of communication. Their voice and SMS logs are accessible in network switches and/or telecommunication centers. However, relationships like friendship have been mined from several features that are hard or impossible...
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