Interpolating Topics in Wikipedia by Imitating Conversation Logs


Interpolating Topics in Wikipedia by Imitating Conversation Logs – The paper presents an efficient algorithm to recognize the most influential topics in the Wikipedia. We use this method to identify topics in Wikipedia as influential among the topics in other articles in the article. In the Wikipedia, we learn topic models that predict topics in some articles, but ignore them in others. Hence, we need to model the interactions between different topics in the article. We propose a novel approach which learns a topic model that is consistent in each article and generalizes well to many articles, without requiring any prior knowledge about the articles. The approach is shown to be general and can be applied to any topic model.

We study the problem of estimating the expected utility of a system when its information about its environment (i.e., its utility or cost) is spatially and temporally bounded. The goal of this study is to understand the utility properties of a system that is observed to generate high-quality human-readable text reports. One method to learn such models is to use a sparse Markov chain Monte Carlo sequence. As well as the system’s environment, we use the information as a covariate which has to be processed by different models using different data types. The most common method is a Bayesian Network. However, the Bayesian model assumes that the uncertainty in the data is non-linear, is unable to handle uncertainty in the input data, or is slow to learn a model. In this paper, we propose a novel learning method that simultaneously learns a Bayesian network and the information in the input data. The proposed method is efficient in achieving high accuracy in a low-parameter setting. We demonstrate the usefulness of our method on several real-world tasks.

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Interpolating Topics in Wikipedia by Imitating Conversation Logs

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  • Deep Neural Networks and Multiscale Generalized Kernels: Generalization Cost Benefits

    Nonlinear Bayesian Networks for Predicting Human Performance in Imprecisely-Toward-Probabilistic-Learning-TimeWe study the problem of estimating the expected utility of a system when its information about its environment (i.e., its utility or cost) is spatially and temporally bounded. The goal of this study is to understand the utility properties of a system that is observed to generate high-quality human-readable text reports. One method to learn such models is to use a sparse Markov chain Monte Carlo sequence. As well as the system’s environment, we use the information as a covariate which has to be processed by different models using different data types. The most common method is a Bayesian Network. However, the Bayesian model assumes that the uncertainty in the data is non-linear, is unable to handle uncertainty in the input data, or is slow to learn a model. In this paper, we propose a novel learning method that simultaneously learns a Bayesian network and the information in the input data. The proposed method is efficient in achieving high accuracy in a low-parameter setting. We demonstrate the usefulness of our method on several real-world tasks.


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