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Explicit Duration Recurrent Networks.

Shun-Zheng Yu

    IEEE Transactions on Neural Networks and Learning Systems
    |January 26, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Explicit Duration Recurrent Network (EDRN), a novel recurrent neural network (RNN) that models variable hidden state durations. EDRN offers improved performance and interpretability over traditional LSTMs and GRUs.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Recurrent Neural Networks (RNNs) excel at sequence processing but struggle with variable hidden state dwell times.
    • Existing RNN variants like LSTM and GRU lack explicit mechanisms for modeling state duration distributions.

    Purpose of the Study:

    • To interpret conventional RNNs (RNN, LSTM, GRU) through the lens of Hidden Markov Models (HMMs).
    • To propose a novel RNN, the Explicit Duration Recurrent Network (EDRN), inspired by Hidden Semi-Markov Models (HSMMs).
    • To enable explicit modeling of hidden state duration distributions and enhance model interpretability.

    Main Methods:

    • Interpreting standard RNNs, LSTMs, and GRUs using an extended Hidden Markov Model (HMM) framework.
    • Developing the Explicit Duration Recurrent Network (EDRN) as an extension analogous to Hidden Semi-Markov Models (HSMMs).

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  • Evaluating EDRN's performance against conventional LSTMs.

    • Main Results:

    • EDRN demonstrates superior performance compared to standard Long Short-Term Memory (LSTM) networks.
    • EDRN explicitly models arbitrary duration distribution functions for hidden states.
    • Model parameters in EDRN are interpretable, allowing inference of quantities not accessible by conventional RNNs.
    • Conventional RNNs (LSTM, GRU) can be modified for performance gains without parameter increase.

    Conclusions:

    • The Explicit Duration Recurrent Network (EDRN) effectively models variable hidden state durations, outperforming LSTMs.
    • EDRN enhances the interpretability of RNNs and enables new analytical capabilities.
    • The findings suggest potential for improved performance in existing RNN architectures through minor modifications.