Tabular classification has traditionally relied on supervised algorithms,
which estimate the parameters of a prediction model using its training data.
Recently, Prior-Data Fitted Networks (PFNs) such as TabPFN have successfully
learned to classify tabular data in-context: the model parameters are designed
to classify new samples based on labelled training samples given after the
model training. While such models show great promise, their applicability to
real-world data remains limited due to the computational scale needed. Here we
study the following question: given a pre-trained PFN for tabular data, what is
the best way to summarize the labelled training samples before feeding them to
the model? We conduct an initial investigation of sketching and
feature-selection methods for TabPFN, and note certain key differences between
it and conventionally fitted tabular models.

给定一种预先训练的 TabPFN 对于表格数据，如何将标注的训练样本进行摘要以便输入模型是最佳的方法？我们对 TabPFN 进行了定购和特征选择方法的初步研究，并注意到它与传统拟合表格模型之间的某些关键差异。

为表格先验数据拟合网络缩放的草图和特征选择

Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data  Fitted Networks

This paper comprehensively surveys the development of trajectory clustering.
Considering the critical role of trajectory data mining in modern intelligent
systems for surveillance security, abnormal behavior detection, crowd behavior
analysis, and traffic control, trajectory clustering has attracted growing
attention. Existing trajectory clustering methods can be grouped into three
categories: unsupervised, supervised and semi-supervised algorithms. In spite
of achieving a certain level of development, trajectory clustering is limited
in its success by complex conditions such as application scenarios and data
dimensions. This paper provides a holistic understanding and deep insight into
trajectory clustering, and presents a comprehensive analysis of representative
methods and promising future directions.

本文综述了轨迹聚类的发展，探讨了其在智能系统中的关键作用，归纳了三种聚类方法，分析了其限制因素与未来方向。