Algebraic reconfiguration of LSTM network for automated video data stream analytics using applied machine learning
Petr Andreevich Pylov, Roman Viacheslavovich Maitak, Andrey Protodyakonov · E3S Web of Conferences · 2023
Recurrent neural networks (RNNs) are a powerful tool for processing sequential data. However, the standard LSTM architecture, despite its effectiveness in capturing long-range dependencies, can still encounter some problems when dealing with particularly complex sequences. In this paper, we present a mathematical modification of LSTM that extends the basic advantages of the long short-term memory model and will help to model complex dependencies in data more accurately.