Recurrent Neural Networks are powerful machine learning frameworks that allow for data to be saved and referenced in a temporal sequence. This opens many new possibilities in fields such as handwriting analysis and speech recognition. This paper seeks to explore current research being conducted on RNNs in four very important areas, being biometric authentication, expression recognition, anomaly detection, and applications to aircraft. This paper reviews the methodologies, purpose, results, and the benefits and drawbacks of each proposed method below. These various methodologies all focus on how they can leverage distinct RNN architectures such as the popular Long Short-Term Memory (LSTM) RNN or a Deep-Residual RNN. This paper also examines which frameworks work best in certain situations, and the advantages and disadvantages of each pro-posed model.
翻译:经常性神经网络是强大的机器学习框架,使得数据能够按时间顺序保存和引用。这在笔迹分析和语音识别等领域开辟了许多新的可能性。本文件试图探讨目前在四个非常重要的领域对区域NN进行的研究,即生物鉴别认证、表达识别、异常探测和飞机应用。本文回顾了以下每一种拟议方法的方法、目的、结果和利弊。这些不同方法都侧重于如何利用不同的区域NN结构,如流行的长短期内存(LSTM) RNN或深Residual RNN。本文还审视了在某些情况下哪些框架最有效,以及每个赞成模式的利弊。