The Long Short-Term Memory (LSTM) layer is an important advancement in the
field of neural networks and machine learning, allowing for effective training
and impressive inference performance. LSTM-based neural networks have been
successfully employed in various applications such as speech processing and
language translation. The LSTM layer can be simplified by removing certain
components, potentially speeding up training and runtime with limited change in
performance. In particular, the recently introduced variants, called SLIM
LSTMs, have shown success in initial experiments to support this view. Here, we
perform computational analysis of the validation accuracy of a convolutional
plus recurrent neural network architecture using comparatively the standard
LSTM and three SLIM LSTM layers. We have found that some realizations of the
SLIM LSTM layers can potentially perform as well as the standard LSTM layer for
our considered architecture.

本研究使用计算分析验证了标准 LSTM 和三种 SLIM LSTM 层性能的比较，发现其中某些 SLIM LSTM 层可以在卷积加循环神经网络架构中与标准 LSTM 层的表现相当。