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Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding.

Zhibin Yu¹, Dennis S Moirangthem², Minho Lee²

¹Department of Electrical Engineering, College of Information Science and Engineering, Ocean University of ChinaQingdao, China.

Frontiers in Neurorobotics

|September 8, 2017

Summary

This summary is machine-generated.

We developed a Continuous Timescale Long-Short Term Memory (CTLSTM) model to better understand human intentions from action sequences. This new model effectively captures long-term context, overcoming limitations of previous methods for complex tasks.

Keywords:

LSTM classification continuous timescale dynamic sequence recurrent neural network

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Area of Science:

Artificial Intelligence
Machine Learning
Cognitive Science

Background:

Understanding human intention from action sequences is challenging due to the need for long-term context analysis.
Conventional Multiple Timescales Recurrent Neural Network (MTRNN) models struggle with longer sequences due to the vanishing gradient problem.

Purpose of the Study:

To propose a novel deep learning model, Continuous Timescale Long-Short Term Memory (CTLSTM), for improved human intention recognition.
To address the limitations of MTRNN in capturing long-term dependencies in action sequences.

Main Methods:

Inherited the multiple timescales concept into Long-Short Term Memory (LSTM) recurrent neural networks (RNNs).
Introduced an additional recurrent connection in LSTM cell outputs to create a time-delay for capturing slow context.
Evaluated the model on multiple large dataset classification tasks.

Main Results:

The CTLSTM model demonstrated superior context modeling ability compared to conventional methods.
The model effectively captured dynamic features across multiple large datasets.
Performance improvements were observed in tasks requiring the understanding of longer action sequences.

Conclusions:

The multiple timescales concept significantly enhances the ability of LSTM networks to handle longer sequences.
CTLSTM is well-suited for complex tasks like human intention recognition that require understanding extended action patterns.
The proposed model offers a promising solution for analyzing dynamic human behaviors and inferring intentions.