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 discuss theoretical approaches to modeling stochastic process and learning the posterior parameters via stochastic process learning. In particular we are interested in stochastic process learning based on the assumption that the stochastic process is a linear function. In stochastic process learning, we show that stochastic process learning can be useful for learning posterior parameters. It is shown that a similar model can be used to model a stochastic process in different settings without any restriction on the parameter size. We also propose two new algorithms that approximate stochastic process learning. As a first step towards the development of algorithms for stochastic process optimization that generalize stochastic process learning to stochastic process learning, we develop variational neural networks (VNNs), a general framework that incorporates stochastic process and learning as two distinct processes. We demonstrate on real data sets that VNNs are able to outperform stochastic process and learn state-space parameters better than stochastic process learning in terms of expected learning error (LERM) on several benchmark problems.

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# Interpolating Topics in Wikipedia by Imitating Conversation Logs

Unsupervised Estimation of Hidden Markov Random Field with Multiplicative Noise ModelWe discuss theoretical approaches to modeling stochastic process and learning the posterior parameters via stochastic process learning. In particular we are interested in stochastic process learning based on the assumption that the stochastic process is a linear function. In stochastic process learning, we show that stochastic process learning can be useful for learning posterior parameters. It is shown that a similar model can be used to model a stochastic process in different settings without any restriction on the parameter size. We also propose two new algorithms that approximate stochastic process learning. As a first step towards the development of algorithms for stochastic process optimization that generalize stochastic process learning to stochastic process learning, we develop variational neural networks (VNNs), a general framework that incorporates stochastic process and learning as two distinct processes. We demonstrate on real data sets that VNNs are able to outperform stochastic process and learn state-space parameters better than stochastic process learning in terms of expected learning error (LERM) on several benchmark problems.