MIDA: Multiple Imputation Models and Acceleration of Inference


MIDA: Multiple Imputation Models and Acceleration of Inference – We present an efficient algorithm for the semi-supervised learning (SSL) problem of estimating the value of an unknown quantity. Our algorithm is a simple and effective algorithm to solve the first stage, that requires no machine-learning or domain modeling involved. The algorithm can be efficiently compared with most existing algorithms for both semi-supervised and supervised learning tasks. Besides, we show that this algorithm is very easy to implement and work on.

In this paper, we propose an end-to-end method for predicting Alzheimer’s Disease (AD) in brain tissue by using unsupervised learning. In this paper, an attention based classifier (AD-C) is proposed for Alzheimer’s Disease (DAD) prediction, based on a deep feature-based model which can learn the visual features of the brain regions that are related with AD. Moreover, an Alzheimer’s DAD prediction model is trained by using the spatial domain feature representation based on the spatial relationship between features. Moreover, a deep feature-based classifier is used as the model by using a recurrent network in the deep data and a recurrent neural network. Experiments on two different AD datasets have been performed to evaluate the performance of the proposed approach. The obtained results demonstrate that the proposed AD-C model can improve the performance of the proposed AD-C model prediction method.

An empirical evaluation of Bayesian ensemble learning for linear models

Distributed Stochastic Optimization for Conditional Random Fields

MIDA: Multiple Imputation Models and Acceleration of Inference

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  • A Bayesian Nonparametric Bayes Approach to Dynamic Dynamic Network Learning

    Classification of Brain Areas Using Convolutional Neural NetworksIn this paper, we propose an end-to-end method for predicting Alzheimer’s Disease (AD) in brain tissue by using unsupervised learning. In this paper, an attention based classifier (AD-C) is proposed for Alzheimer’s Disease (DAD) prediction, based on a deep feature-based model which can learn the visual features of the brain regions that are related with AD. Moreover, an Alzheimer’s DAD prediction model is trained by using the spatial domain feature representation based on the spatial relationship between features. Moreover, a deep feature-based classifier is used as the model by using a recurrent network in the deep data and a recurrent neural network. Experiments on two different AD datasets have been performed to evaluate the performance of the proposed approach. The obtained results demonstrate that the proposed AD-C model can improve the performance of the proposed AD-C model prediction method.


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