Time-aware long short-term memory

Time-aware LSTM (T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference.^[1] Experiments using real and synthetic data proved that T-LSTM auto-encoder outperformed widely used frameworks including LSTM and MF1-LSTM auto-encoders.^{[citation needed]}

References

↑ Organizers, KDD. "Patient Subtyping via Time-Aware LSTM Networks". www.kdd.org.

This artificial neural network-related article is a stub. You can help Wikipedia by adding missing information.

[1] Organizers, KDD. "Patient Subtyping via Time-Aware LSTM Networks". www.kdd.org.

[1]

A page that knows where it met you.

References