Description
The paper Recurrent Neural Network Regularization by Zaremba, Sutskever, and Vinyals (2014) introduces a novel approach to applying dropout in Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Traditional dropout techniques, effective in feedforward networks, often fail in RNNs due to the recurrent connections amplifying noise, which can hinder learning. The authors propose a method to apply dropout selectively within LSTMs, demonstrating its efficacy in reducing overfitting across various tasks, including language modeling, speech recognition, image caption generation, and machine translation.
