LSTM models are widely used for ... can help mitigate overfitting when using a larger number of units. In general, the more complex our model architecture, the better the model performance ...

HybridNER is a Named Entity Recognition (NER) model that combines the strengths of Transformer and LSTM architectures. This hybrid approach allows the model to effectively capture both long-term ...

For example, an LSTM or GRU model can recognize the words spoken by a user, or synthesize speech from text, by applying a sequence-to-sequence architecture ... and learn from complex and dynamic ...

Abstract: The long short term memory (LSTM) network is a popular deep learning model with a wide range of applications. Skip connection is a promising and important architectural innovation that can ...