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Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
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Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks
The widespread use of Batch Normalization has enabled training deeper ne...
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Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization
Feature selection is generally used as one of the most important pre-pro...
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Consistent Batch Normalization for Weighted Loss in Imbalanced-Data Environment
In this study, we consider classification problems based on neural netwo...
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A Novel Convolutional Neural Network for Image Steganalysis with Shared Normalization
Deep learning based image steganalysis has attracted increasing attentio...
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Online Learning in Planar Pushing with Combined Prediction Model
Pushing is a useful robotic capability for positioning and reorienting o...
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Mean Spectral Normalization of Deep Neural Networks for Embedded Automation
Deep Neural Networks (DNNs) have begun to thrive in the field of automat...
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SPOC learner's final grade prediction based on a novel sampling batch normalization embedded neural network method
Recent years have witnessed the rapid growth of Small Private Online Courses (SPOC) which is able to highly customized and personalized to adapt variable educational requests, in which machine learning techniques are explored to summarize and predict the learner's performance, mostly focus on the final grade. However, the problem is that the final grade of learners on SPOC is generally seriously imbalance which handicaps the training of prediction model. To solve this problem, a sampling batch normalization embedded deep neural network (SBNEDNN) method is developed in this paper. First, a combined indicator is defined to measure the distribution of the data, then a rule is established to guide the sampling process. Second, the batch normalization (BN) modified layers are embedded into full connected neural network to solve the data imbalanced problem. Experimental results with other three deep learning methods demonstrates the superiority of the proposed method.
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