Researchers from Shanghai Artificial Intelligence Laboratory and MIT Unveil Hierarchically Gated Recurrent Neural Network RNN: A New Frontier in Efficient Long-Term Dependency Modeling

The Hierarchically Gated Recurrent Neural Network (HGRN) technique developed by researchers from the Shanghai Artificial Intelligence Laboratory and MIT CSAI addresses the challenge of enhancing sequence modeling by incorporating forget gates in linear RNNs. The aim is to enable upper layers to capture long-term dependencies while allowing lower layers to focus on short-term dependencies, especially […]

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