Ensemble of Multilayer Neural Networks for Diagnosis of Neuromuscular Disorders


Ensemble of Multilayer Neural Networks for Diagnosis of Neuromuscular Disorders – In this article, we study the performance of recurrent neural networks (RNNs) to help in diagnosis of patients. We demonstrate that the proposed architecture enables patient evaluation via a simple visual classification task. We present preliminary results to validate this capability. Based on the clinical experience, we propose a RNN architecture based on a multilayer perceptron-based model. The proposed architecture performs a deep learning based recognition task to classify patients. Based on the RNN network, we further suggest that the RNN network can be a suitable alternative to a deep RNN as we can predict the semantic similarity of patients with different disease types.

We present the first real-time and scalable approach for the problem of predicting the outcome of a simulated election. The prediction is performed during the course of a daily election. One particular feature of this challenge is that even for a few days, the forecast accuracy of the prediction is always significantly lower than that of the observed forecast.

We consider the problem of extracting salient and unlabeled visual features from a text, and thus derive an approach that works well for image classification. As the task is multi-dimensional, we design a deep Convolutional Neural Network (CNN) that learns a visual feature descriptor by minimizing an image’s weighting process using the sum of the global feature maps, then learning a descriptor from the weights in the final feature map. We illustrate the effectiveness of CNNs trained on the Penn Treebank (TT) dataset and a standard benchmark task for image classification.

The Effectiveness of Multitask Learning in Deep Learning Architectures

Stochastic Dual Coordinate Ascent with Deterministic Alternatives

Ensemble of Multilayer Neural Networks for Diagnosis of Neuromuscular Disorders

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  • Learn, Adapt and Scale with Analogies and Equivalences

    Predicting Daily Activity with a Deep Neural NetworkWe present the first real-time and scalable approach for the problem of predicting the outcome of a simulated election. The prediction is performed during the course of a daily election. One particular feature of this challenge is that even for a few days, the forecast accuracy of the prediction is always significantly lower than that of the observed forecast.

    We consider the problem of extracting salient and unlabeled visual features from a text, and thus derive an approach that works well for image classification. As the task is multi-dimensional, we design a deep Convolutional Neural Network (CNN) that learns a visual feature descriptor by minimizing an image’s weighting process using the sum of the global feature maps, then learning a descriptor from the weights in the final feature map. We illustrate the effectiveness of CNNs trained on the Penn Treebank (TT) dataset and a standard benchmark task for image classification.


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