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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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StaRS: Learning a Stable Representation Space for Continual Relation Classification.

Ning Pang, Xiang Zhao, Weixin Zeng

    IEEE Transactions on Neural Networks and Learning Systems
    |August 23, 2024
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    Summary
    This summary is machine-generated.

    This study introduces a novel two-stage approach to improve continual relation classification (CRC) models. By stabilizing representation space during adaptation and balancing decision boundaries, the method enhances performance in knowledge graph construction.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Relation classification (RC) is crucial for automatic knowledge graph construction.
    • Continual learning settings are increasingly used for RC due to evolving relation types.
    • Existing methods struggle with representation space distortion during adaptation in continual RC (CRC).

    Purpose of the Study:

    • To address the representation space distortion in continual relation classification.
    • To enhance the stability and performance of CRC models in dynamic environments.
    • To develop a robust two-stage training paradigm for continual relation classification.

    Main Methods:

    • Proposed a two-stage training strategy incorporating knowledge distillation and a margin loss for stable adaptation.
    • Introduced a self-contrastive learning objective in the second stage to balance the decision boundary.
    • Focused on maintaining representation space stability during the adaptation to new relations.

    Main Results:

    • The proposed model demonstrated superior performance compared to existing methods in various continual relation classification settings.
    • Experimental results validate the effectiveness of the knowledge distillation, margin loss, and self-contrastive learning components.
    • The tailored designs successfully achieved better performance in continual relation classification.

    Conclusions:

    • The developed two-stage approach effectively mitigates catastrophic forgetting in continual relation classification.
    • The method achieves a stable representation space, crucial for encoding instances in dynamic settings.
    • This work offers a significant advancement in continual learning for knowledge graph construction.