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There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
An individual's personality determines the probable repertoire of their reactions to a particular situation. A social robot is much more effective if it is able to learn and so take into account the properties of the humans around it, including personalities. We investigate how well personality can be estimated based on modest amounts of speech or writing, which a social robot might (over)hear. Such...
Trading strategies basing on both financial analysis and machine learning techniques are becoming increasingly popular due to their ability to capture micro market price movements and leverage big data. An important class of works are focusing on exploiting the structural relationships between companies for accurate stock price prediction. In this paper we develop an algorithm for learning the parameters...
Feature selection is the process of selecting a subset of relevant features from the larger set of collected features. As the amount of available data grows with technology, feature selection becomes a more important part of the system-design process. In real-world applications, there are several costs associated with the collection, processing, and storage of data. Given that these costs can vary...
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting,...
The special characteristics of time series data, such as their high dimensionality and complex dependencies between variables make the problem of detecting anomalies in time series very challenging. Anomalies and more precisely dependency anomalies ensue from the temporal causal depen-dencies. Furthermore the graphical Granger causal models provide an appropriate environment to capture all the temporal...
The most significant trend in human creativity is the shift from individual to teams. Great achievements across academic disciplines and industries are increasingly teamwork. Motivated by this, we aim to uncover teamwork in networks, including predicting teams' performance, optimizing teams' compositions and explaining the prediction results and optimization actions.
The recent rise in the use of social networks has resulted in an abundance of information on different aspects of everyday social activities that is available online. In the process of analysis of identifying the information originating from social networks, and especially Twitter, an important aspect is that of the geographic coordinates, i.e., geolocalisation, of the relevant information. Geolocalized...
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