Fast Convergence of Bayesian Networks via Bayesian Network Kernels


Fast Convergence of Bayesian Networks via Bayesian Network Kernels – Recently several methods of learning Bayesian distributions based on Bayesian networks have been proposed. In most of the literature the approach assumes that an algorithm that is applicable to the Bayesian network has a probabilistic model. Unfortunately, there are also several drawbacks to this assumption. (i) Probabilistic models are not suitable for learning Bayesian networks in general, and (ii) Bayesian networks are difficult to train (e.g. as Bayesian networks). In this work we will present an approach to developing an algorithm to predict posterior probability distributions from Bayesian networks by using both probabilistic models and Bayesian networks. The key result is that Bayesian networks can be trained from a probabilistic model but not the posterior probability distributions. We will provide a detailed technical analysis of both algorithms and discuss the theoretical implications of our approach.

This paper proposes a novel method for real-time brain network prediction, via a learning-to-learn paradigm. A supervised learning framework is then developed to perform multi-task, multi-context learning. To handle the need to model long-term dependencies, we propose an iterative update of the neural network, which in turn leverages local recurrent connections to learn to predict long-term connections. The iterative framework, which we call a recurrent-bisterent framework, uses the output of a pair-wise graph to predict the most relevant local connections based on their correlation and is robust to variations in parameters. Moreover, by using the prediction results from this framework, a training set of long-term brain connections is obtained. The proposed method is evaluated on several benchmark data sets, showing that our method has high predictive performance and provides good computational power.

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Fast Convergence of Bayesian Networks via Bayesian Network Kernels

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  • Generating More Reliable Embeddings via Semantic Parsing

    EgoModeling: Real-time Modelling of Brain ConnectionsThis paper proposes a novel method for real-time brain network prediction, via a learning-to-learn paradigm. A supervised learning framework is then developed to perform multi-task, multi-context learning. To handle the need to model long-term dependencies, we propose an iterative update of the neural network, which in turn leverages local recurrent connections to learn to predict long-term connections. The iterative framework, which we call a recurrent-bisterent framework, uses the output of a pair-wise graph to predict the most relevant local connections based on their correlation and is robust to variations in parameters. Moreover, by using the prediction results from this framework, a training set of long-term brain connections is obtained. The proposed method is evaluated on several benchmark data sets, showing that our method has high predictive performance and provides good computational power.


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