Algebraic reconfiguration of LSTM network for automated video data stream analytics using applied machine learning
Year: 2023
Type: conference-paper
Abstract: 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.
Source: E3S Web of Conferences
Authors: Petr Pylov, Roman Maitak, Andrey Protodyakonov
Institution: Kuzbass State Technical University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4389471969