Based on the architecture of the single-task model, the multi-task model contains two main parts: shared layers for learning general hidden features from all data and task-specific layers for learning specific weights for different tasks [34]. Here, we have two different tasks: binary classification and regression. The input, feature extraction and concatenation parts are similar to those of the single-task model. The loss functions for different tasks are defined as: binary cross-entropy for classification (Loss1) and the MSE with L2 regularization for regression (Loss2).

where N, M and f(), and g() correspond to the samples and models for the classification and regression tasks; xi and yi correspond to the input and labels, respectively. ||w|| is an L2 regularization term; and λ ≥ 0 is used to adjust the relationship between the empirical risk and regularization term of the regression task.

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