In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system.

Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks.

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.

The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.

The behavior emerges and becomes more robust as the architecture scales up its number of parameters.

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.