论文标题
基于神经网络的方法，用于转移学习用于遗传数据分析
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis
论文作者
论文摘要
在许多应用程序问题（例如计算机视觉和自然语言处理）中，转移学习已成为一种强大的技术。但是，在应用遗传数据分析时，该技术在很大程度上被忽略了。在本文中，我们将转移学习技术与基于神经网络的方法（预期神经网络）相结合。通过转移学习，我们不是从头开始学习过程，而是从解决不同任务时学习的一项任务开始。我们利用先前的学习，并避免从头开始，以通过在不同但相关的任务中获得的信息来提高模型性能。为了演示性能，我们运行两个真实的数据集。通过使用转移学习算法，与预期神经网络相比，预期神经网络的性能得到了改善，而无需使用转移学习技术。
Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing. However, this technique is largely ignored in application to genetic data analysis. In this paper, we combine transfer learning technique with a neural network based method(expectile neural networks). With transfer learning, instead of starting the learning process from scratch, we start from one task that have been learned when solving a different task. We leverage previous learnings and avoid starting from scratch to improve the model performance by passing information gained in different but related task. To demonstrate the performance, we run two real data sets. By using transfer learning algorithm, the performance of expectile neural networks is improved compared to expectile neural network without using transfer learning technique.
