Linzhe Zhang, Changming Xu
This paper introduces a framework to analyze what information a recurrent model can safely forget while preserving its predictive capabilities. It establishes a boundary between discrete and continuous predictive states and develops an auditable finite-future framework. The framework provides guarantees for bounded audit-semantic distortion.
The paper's contribution has practical significance in ensuring the reliability and stability of recurrent models in real-world applications. It provides a way to certify the separation margin of a model's predictions, which is crucial for safety-critical systems. The findings can be applied to various areas, including natural language processing and time-series forecasting.
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