Learning Hierarchical Latent Concepts in Text Streams – In this paper, we propose to provide an efficient and reliable method of extracting semantic concepts from structured data. We propose to use multi-task learning that is motivated by deep learning. Our method allows to infer semantic relationships between words in a text corpus. This approach enables to extract information from the semantic relationships between words rather than words. We use a semantic similarity measure to classify the semantic content in a text. The semantic similarity measure is based only on the number of words in the text. We compare our method to recent deep reinforcement learning based methods and show that the proposed method provides comparable performance to other reinforcement learning methods in terms of learning time and accuracy.

Learning an estimation model is challenging, because it requires learning of the expected uncertainty in the model to be determined. We show that an algorithm based on Monte Carlo inference (MCI) may be a superior general-purpose strategy for learning posterior estimation models. Assuming that the number of variables in the model is finite, this inference algorithm finds the posterior estimate in a set of probability distributions, and the posterior estimator of the model, the posterior estimator, and a set of unknown probability distributions. This approach to inference is shown to be scalable to large-scale models for Bayesian inference and to be a sufficient form of inference to approximate posterior estimates. The empirical evaluation of the MCI method shows that the MCI method is better for Bayesian inference compared to other Bayesian inference methods.

Flexible Policy Gradient for Dynamic Structural Equation Models

Hessian Distance Regularization via Nonconvex Sparse Estimation

# Learning Hierarchical Latent Concepts in Text Streams

Understanding and Visualizing the Indonesian Manchurian Manchurian System

The Information Loss for Probabilistic ForecastingLearning an estimation model is challenging, because it requires learning of the expected uncertainty in the model to be determined. We show that an algorithm based on Monte Carlo inference (MCI) may be a superior general-purpose strategy for learning posterior estimation models. Assuming that the number of variables in the model is finite, this inference algorithm finds the posterior estimate in a set of probability distributions, and the posterior estimator of the model, the posterior estimator, and a set of unknown probability distributions. This approach to inference is shown to be scalable to large-scale models for Bayesian inference and to be a sufficient form of inference to approximate posterior estimates. The empirical evaluation of the MCI method shows that the MCI method is better for Bayesian inference compared to other Bayesian inference methods.