A Hybrid Metaheuristic for Learning Topic-space Representations


A Hybrid Metaheuristic for Learning Topic-space Representations – We show that a well-tuned and deep learning-driven search is sufficient to extract a set of meaningful sentences from a set of sentence sources. While supervised learning can be successfully adopted to explore, to mine the knowledge from a source, it is essential to learn from its knowledge, which is often very rich and highly unstructured. This model involves two phases, a semantic search that consists of retrieving information about the source and a semantic search that extracts the semantic knowledge from that source. In our model and in the literature, we extract meaningful sentences of natural language, using a deep neural network. In this model we use a CNN that is trained to learn to predict sentence representations from the source. We show that the semantic and semantic features extracted in the CNN are relevant and we can learn a model that generalises to non-human subjects. The model can be further used for semantic search in both training and evaluation. We also discuss the effect of using this model on a standard CNN evaluation tool on the test set.

A method to predict a traffic event from a prediction of a traffic flowchart is presented here. In addition, we present a model that utilizes the predictions of a few traffic event instances to estimate the expected outcome and perform a prediction that is consistent with the traffic flows. The prediction is learned from the event instances and the prediction is used to optimize a decision tree with a desired outcome. The proposed method utilizes an appropriate distance metric for decision trees trained on street scene data to make it more accurate. The prediction is made from the data extracted from a pedestrian traffic flow chart and the results are compared with the prediction with the road traffic data obtained from a vehicle traffic chart. Experiments show that the learning performance is comparable to two-way street traffic prediction (two-way) in both scenarios. The proposed method demonstrates the usefulness of distance metric for traffic prediction.

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A Hybrid Metaheuristic for Learning Topic-space Representations

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  • Neural Architectures of Visual Attention

    Training a Neural Network for Detection of Road Traffic FlowchartA method to predict a traffic event from a prediction of a traffic flowchart is presented here. In addition, we present a model that utilizes the predictions of a few traffic event instances to estimate the expected outcome and perform a prediction that is consistent with the traffic flows. The prediction is learned from the event instances and the prediction is used to optimize a decision tree with a desired outcome. The proposed method utilizes an appropriate distance metric for decision trees trained on street scene data to make it more accurate. The prediction is made from the data extracted from a pedestrian traffic flow chart and the results are compared with the prediction with the road traffic data obtained from a vehicle traffic chart. Experiments show that the learning performance is comparable to two-way street traffic prediction (two-way) in both scenarios. The proposed method demonstrates the usefulness of distance metric for traffic prediction.


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