In-context system identification aims at constructing meta-models to describe
classes of systems, differently from traditional approaches that model single
systems. This paradigm facilitates the leveraging of knowledge acquired from
observing the behaviour of different, yet related dynamics. This paper
discusses the role of meta-model adaptation. Through numerical examples, we
demonstrate how meta-model adaptation can enhance predictive performance in
three realistic scenarios: tailoring the meta-model to describe a specific
system rather than a class; extending the meta-model to capture the behaviour
of systems beyond the initial training class; and recalibrating the model for
new prediction tasks. Results highlight the effectiveness of meta-model
adaptation to achieve a more robust and versatile meta-learning framework for
system identification.

构建元模型描述系统类别的背景下系统识别的角色和元模型自适应的作用以及如何通过数字示例提高预测性能

关于系统识别中基于上下文学习器的适应性

On the adaptation of in-context learners for system identification

Transfer learning is an important step to extract meaningful features and
overcome the data limitation in the medical Visual Question Answering (VQA)
task. However, most of the existing medical VQA methods rely on external data
for transfer learning, while the meta-data within the dataset is not fully
utilized. In this paper, we present a new multiple meta-model quantifying
method that effectively learns meta-annotation and leverages meaningful
features to the medical VQA task. Our proposed method is designed to increase
meta-data by auto-annotation, deal with noisy labels, and output meta-models
which provide robust features for medical VQA tasks. Extensively experimental
results on two public medical VQA datasets show that our approach achieves
superior accuracy in comparison with other state-of-the-art methods, while does
not require external data to train meta-models.

本论文提出了一种新的多元元模型量化方法，能够有效地学习元注释和利用有意义的特征以处理医学可视问题回答任务中的数据限制问题。实验表明，该方法不需要外部数据即可训练元模型，并且在两个公共医学 VQA 数据集上得到比其他现有方法更为准确的结果。