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DUAL RECURRENT NEURAL NETWORK ARCHITECTURE FOR MODELING LONG-TERM DEPENDENCIES IN SEQUENTIAL DATA
DUAL RECURRENT NEURAL NETWORK ARCHITECTURE FOR MODELING LONG-TERM DEPENDENCIES IN SEQUENTIAL DATA
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机译：双重经常性神经网络架构，用于在顺序数据中建模长期依赖性
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摘要
Learning the dynamics of an environment and predicting consequences in the future is a recent technical advancement that can be applied to video prediction, speech recognition, among other applications. Generally, machine learning, such as deep learning models, neural networks, or other artificial intelligence algorithms are used to make the predictions. However, current artificial intelligence algorithms used for making predictions are typically limited to making short-term future predictions, mainly as a result of 1) the presence of complex dynamics in high-dimensional video data, 2) prediction error propagation over time, and 3) inherent uncertainty of the future. The present disclosure enables the modeling of long-term dependencies in sequential data for use in making long-term predictions by providing a dual (i.e. two-part) recurrent neural network architecture.
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