Learning Mixtures of Discrete Distributions in Recurrent Networks


Learning Mixtures of Discrete Distributions in Recurrent Networks – In this paper we give a systematic analysis of the optimal model selection technique in the literature, with application to the problems of online decision problem formulation and Bayesian inference. A key question to be addressed in this work is to evaluate the model selection technique based on the information theoretic model of learning. In particular, we analyze Bayesian inference based on a general framework of probabilistic inference to learn a posterior conditional model for a given input parameter. The problem of Bayesian inference based on Bayes’ decision problem formulation is posed. We propose an efficient algorithm for Bayesian inference, where the goal is to select the desired model that maximizes the expected posterior distribution. We show that the algorithm is optimal to learn the model, because it is an adaptive selection technique, and so it can learn the posterior conditional model (i.e. of the parameters in the Bayes’ decision problem) that maximizes the expected posterior distribution. We provide theoretical and numerical results using a general model selection problem formulation and show that inference based on Bayes’ decision problem formulation can be efficiently executed in various ways.

Learning a phrase from a sequence of phrases is a challenging task, and recent research aims at addressing the problem of phrase-learning. However, most existing phrase learning and sentence-based approaches are either manually based or manually-trained. Given large amounts of data on French phrases, we provide a comprehensive list of phrases learning algorithms, and compare the performance of phrase learning and phrase learning methods using a well-known phrase-learning benchmark, the COCO word embedding dataset. Our experiments show that the COCO phrase-learning algorithm outperformed the phrase-learning algorithm by a large margin within the margin of error and with very few outliers, outperforming the phrase-learning algorithm by a small margin as well.

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Identifying and Reducing Human Interaction with Text

Learning Mixtures of Discrete Distributions in Recurrent Networks

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  • Distributed Convex Optimization for Graphs with Strong Convexity

    A Deep Learning Model of French Compound Phrase Bank with Attention-based Model and Lexical PartitioningLearning a phrase from a sequence of phrases is a challenging task, and recent research aims at addressing the problem of phrase-learning. However, most existing phrase learning and sentence-based approaches are either manually based or manually-trained. Given large amounts of data on French phrases, we provide a comprehensive list of phrases learning algorithms, and compare the performance of phrase learning and phrase learning methods using a well-known phrase-learning benchmark, the COCO word embedding dataset. Our experiments show that the COCO phrase-learning algorithm outperformed the phrase-learning algorithm by a large margin within the margin of error and with very few outliers, outperforming the phrase-learning algorithm by a small margin as well.


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