tf-keras
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keras-team
LMU layer, Legendre Memory Units
Describe the problem
LMU's (originally introduced in 2019) are a separate class of RNNs. They have been demonstrated to outperform LSTMs on some tasks that require longer time windows while requiring fewer parameters (see NeurIPS 2019 submission). Including LMU's directly into Keras would extend the usefulness and impact of Keras for its users where parameter size is an important consideration.
@flacle, Could you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you!
Talked this over, I am not sure we would want to go straight to a new layer at this time, but if you are interested, you could contribute a new Keras example (instructions here) showing how to implement this layer, and how it can improve performance on a timeseries dataset over lstms!
This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you.
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This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you.
This issue was closed because it has been inactive for more than 1 year.