Skip to content
Time-Aware LSTM
Python
Branch: master
Clone or download

Latest commit

Latest commit 9380f4b Jun 13, 2019

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Clustering_Data_1D.mat.tar.gz
LICENSE
README.md
Split0.tar.gz Add files via upload Jan 23, 2018
TLSTM.py Update TLSTM.py Jan 23, 2018
T_LSTM_AE.py
example_bash
main.py Update main.py Jan 23, 2018
main_AE.py Update main_AE.py Jan 23, 2018

README.md

Time-Aware Long-Short Term Memory

Regularity of the duration between consecutive elements of a sequence is a property that does not always hold. An architecture that can overcome this irregularity is necessary to increase the prediction performance.

Time Aware LSTM (T-LSTM) was designed to handle irregular elapsed times. T-LSTM is proposed to incorporate the elapsed time information into the standard LSTM architecture to be able to capture the temporal dynamics of sequential data with time irregularities. T-LSTM decomposes memory cell into short-term and long-term components, discounts the short-term memory content using a non-increasing function of the elapsed time, and then combines it with the long-term memory.

Compatibility

Code is compatible with tensorflow version 1.2.1 and Pyhton 2.7.13.

Input Format

An example data format is given where data is stored as a list containing 3 dimensionals tensors such as [number of samples x sequence length x dimensionality].

Reference

Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou, "Patient Subtyping via Time-Aware LSTM Networks", KDD, 2017.

You can’t perform that action at this time.