All tables can be represented as grids. Based on this observation, we propose
GridFormer, a novel approach for interpreting unconstrained table structures by
predicting the vertex and edge of a grid. First, we propose a flexible table
representation in the form of an MXN grid. In this representation, the vertexes
and edges of the grid store the localization and adjacency information of the
table. Then, we introduce a DETR-style table structure recognizer to
efficiently predict this multi-objective information of the grid in a single
shot. Specifically, given a set of learned row and column queries, the
recognizer directly outputs the vertexes and edges information of the
corresponding rows and columns. Extensive experiments on five challenging
benchmarks which include wired, wireless, multi-merge-cell, oriented, and
distorted tables demonstrate the competitive performance of our model over
other methods.

基于表格可以表示为网格的观察，我们提出了 GridFormer，一种通过预测网格的顶点和边来解释不受约束的表格结构的新方法。我们引入了一种 DETR 风格的表格结构识别器，通过一次预测获得网格的多目标信息。对包括有线、无线、多合并单元、定向和扭曲表格在内的五个具有挑战性的基准进行的大量实验证明了我们模型相对于其他方法的竞争性能。

GridFormer：通过格网预测实现准确的表结构识别

GridFormer: Towards Accurate Table Structure Recognition via Grid  Prediction

We introduce a new $(\epsilon_p, \delta_p)$-differentially private algorithm
for the $k$-means clustering problem. Given a dataset in Euclidean space, the
$k$-means clustering problem requires one to find $k$ points in that space such
that the sum of squares of Euclidean distances between each data point and its
closest respective point among the $k$ returned is minimised. Although there
exist privacy-preserving methods with good theoretical guarantees to solve this
problem [Balcan et al., 2017; Kaplan and Stemmer, 2018], in practice it is seen
that it is the additive error which dictates the practical performance of these
methods. By reducing the problem to a sequence of instances of maximum coverage
on a grid, we are able to derive a new method that achieves lower additive
error then previous works. For input datasets with cardinality $n$ and diameter
$\Delta$, our algorithm has an $O(\Delta^2 (k \log^2 n
\log(1/\delta_p)/\epsilon_p + k\sqrt{d \log(1/\delta_p)}/\epsilon_p))$ additive
error whilst maintaining constant multiplicative error. We conclude with some
experiments and find an improvement over previously implemented work for this
problem.

介绍了一种新的差分隐私算法，该算法通过将问题转化为基于网格的最大覆盖问题的一系列实例，实现了较低的加性误差并保持恒定的乘性误差，在 $k$-means 聚类问题上取得了更好的实验效果。