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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Heterogeneous Correlation Aware Regularization for Sequential Confidence Calibration.

Zhenghua Peng, Tianshui Chen, Shuangping Huang

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    Deep sequence recognition models suffer from over-confidence. A new heterogeneous correlation aware sequence regularization (HCSR) method effectively calibrates these models by incorporating correlated sequences during training.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep sequence recognition models exhibit over-confidence, leading to unreliable predictions.
    • Existing calibration methods primarily address classification tasks, leaving sequence recognition models under-addressed.

    Purpose of the Study:

    • To investigate the causes of over-confidence in sequence recognition models.
    • To develop a novel method for calibrating sequence recognition models.

    Main Methods:

    • Identified one-hot encoding as a key factor in model over-confidence.
    • Proposed a heterogeneous correlation aware sequence regularization (HCSR) method.
    • Introduced a correlated sequence mining (CSM) model and an adaptive calibration module.

    Main Results:

    • The HCSR method effectively reduces over-confidence in sequence recognition models.
    • The proposed approach achieves fine-grained calibration by addressing perception and semantic context over-confidence.
    • Experimental results show significant performance improvements over existing methods.

    Conclusions:

    • The HCSR method offers a robust solution for calibrating over-confident sequence recognition models.
    • This work advances the field of model calibration for sequence-based tasks.