On the Utility of the Maximum Entropy Principle for Modeling the Math of Concept Reuse – A novel algorithm for the problem of learning a graph from a large corpus of texts is presented. Given $n$ sentences in English and English-German texts, the resulting graph is drawn from a large corpus of texts and labeled by the word level semantic similarity (SLE) method. In this paper, we formulate the graph-learning problem as two two-fold optimization problem: one is a sparse-sum solution problem, whereas the other is a sum problem to solve efficiently. This leads to a simple and efficient, flexible and accurate algorithm that is capable of solving both problems in the same round. The algorithm is based on a new approach for the SLE problem which addresses the main problem in this paper. Our algorithm shows good results, outperforming the previous two techniques.

There are several recent algorithms for predicting vehicles from data in traffic data streams. In particular, the use of the Lasso is based on solving a very difficult optimization problem, which involves constructing a model of a given data stream using a nonzero sum of the sum of the data. In this paper, we propose an algorithm that combines the optimization and data mining applications of Lasso: We first propose a simple algorithm, called T-LSTM, which is able to be used both as a preprocessing step for the optimisation of the prediction and as a preprocessing function for the optimization of the Lasso. We demonstrate the importance of this approach on the CityScape dataset, and demonstrate several methods for predicting vehicles using T-LSTM.

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# On the Utility of the Maximum Entropy Principle for Modeling the Math of Concept Reuse

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The Application of Bayesian Network Techniques for Vehicle Speed ForecastingThere are several recent algorithms for predicting vehicles from data in traffic data streams. In particular, the use of the Lasso is based on solving a very difficult optimization problem, which involves constructing a model of a given data stream using a nonzero sum of the sum of the data. In this paper, we propose an algorithm that combines the optimization and data mining applications of Lasso: We first propose a simple algorithm, called T-LSTM, which is able to be used both as a preprocessing step for the optimisation of the prediction and as a preprocessing function for the optimization of the Lasso. We demonstrate the importance of this approach on the CityScape dataset, and demonstrate several methods for predicting vehicles using T-LSTM.