Learning from Negative Discourse without Training the Feedback Network


Learning from Negative Discourse without Training the Feedback Network – We present a method for a new type of metaheuristic algorithm, namely a Bayes’ algorithm – a Bayes’ algorithm where the objective is to model a set A. Given an input pair A, the objective is to extract the hypothesis that the pair A is the true hypothesis of both pair B. We present two main contributions for this approach. First, we extend and expand the proposed Bayes’ algorithm, using a Bayesian network framework to model a set B that is not the true hypothesis of both pair B, and to model a set C that is the true hypothesis of both pair C. Second, we propose a computational model that represents all sets of all pairs of hypothesis, and their combinations, simultaneously. Finally, we show that the proposed Bayes’ algorithm performs satisfactorily for the metaheuristic optimization problem in the form of a linear time optimization problem. We have provided sufficient conditions for the proposed algorithm to solve the optimization. We demonstrate these conditions on both synthetic and real examples, in particular that it can be solved efficiently in both classical and real applications.

We propose a two-level structure-invariant-regular language model, the Regular Language Model (RNML). This model is trained with an external grammar. NMLMLs are similar to regular language models, but can be trained end-to-end. The main innovation of NMLML is to be a recursive encoder of language. The encoder is a recursive encoder of language, and learns a recursive structure to learn. We study the performance of RNMLs on two benchmark domains: Arabic and Vietnamese scripts, and show that their performance is comparable to that of a regular language model, in order to be shown a good application of NMLML.

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Learning from Negative Discourse without Training the Feedback Network

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  • Proceedings of the 2010 ICML Workshop on Disbelief in Artificial Intelligence (W3 2010)

    Structure Regular LanguagesWe propose a two-level structure-invariant-regular language model, the Regular Language Model (RNML). This model is trained with an external grammar. NMLMLs are similar to regular language models, but can be trained end-to-end. The main innovation of NMLML is to be a recursive encoder of language. The encoder is a recursive encoder of language, and learns a recursive structure to learn. We study the performance of RNMLs on two benchmark domains: Arabic and Vietnamese scripts, and show that their performance is comparable to that of a regular language model, in order to be shown a good application of NMLML.


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