LSTMs are crucial for tasks involving sequences, such as time series analysis, natural language processing, and speech recognition. Unlike traditional RNNs, which struggle to capture long-term dependencies due to the vanishing gradient problem, LSTMs tackle this issue with their unique structure. They incorporate memory cells that can maintain information in memory for long periods, and three types of gates (input, output, and forget) that regulate the flow of information into and out of the cell, making them adept at learning from data where the gap between relevant information and where it is needed can be large.
Historical overview: The concept of LSTMs was first introduced in 1997 by Sepp Hochreiter and Jürgen Schmidhuber. While RNNs were known for their potential in handling sequential data, their effectiveness was limited by the difficulty in learning long-range dependencies. LSTMs were proposed as a solution to this problem, and over the years, they have gained popularity for their superior performance in various sequential modeling tasks.
Key contributors: The development of LSTMs can be primarily attributed to Sepp Hochreiter and Jürgen Schmidhuber. Their initial work laid the foundation for numerous advancements and variations in LSTM architectures, significantly impacting the field of deep learning and its application to sequence learning problems.