Automated Feature Engineering (AutoFE) has become an important task for any
machine learning project, as it can help improve model performance and gain
more information for statistical analysis. However, most current approaches for
AutoFE rely on manual feature creation or use methods that can generate a large
number of features, which can be computationally intensive and lead to
overfitting. To address these challenges, we propose a novel convolutional
method called FeatGeNN that extracts and creates new features using correlation
as a pooling function. Unlike traditional pooling functions like max-pooling,
correlation-based pooling considers the linear relationship between the
features in the data matrix, making it more suitable for tabular data. We
evaluate our method on various benchmark datasets and demonstrate that FeatGeNN
outperforms existing AutoFE approaches regarding model performance. Our results
suggest that correlation-based pooling can be a promising alternative to
max-pooling for AutoFE in tabular data applications.

自动特征工程（AutoFE）是任何机器学习项目的重要任务，可以帮助改善模型性能并为统计分析提供更多信息。为了解决当前 AutoFE 方法中存在的手动特征创建和特征数量过多问题，我们提出了一种基于相关性的卷积方法 ——FeatGeNN。我们在各种基准数据集上评估了我们的方法，并证明 FeatGeNN 在模型性能方面优于现有的 AutoFE 方法。我们的结果表明，在表格数据应用中，基于相关性的池化可以成为 AutoFE 中替代 max-pooling 的有前景的选择。

FeatGeNN：基于相关性特征提取的表格数据模型性能改进

FeatGeNN: Improving Model Performance for Tabular Data with  Correlation-based Feature Extraction

The current state-of-the-art methods in 3D instance segmentation typically
involve a clustering step, despite the tendency towards heuristics, greedy
algorithms, and a lack of robustness to the changes in data statistics. In
contrast, we propose a fully-convolutional 3D point cloud instance segmentation
method that works in a per-point prediction fashion. In doing so it avoids the
challenges that clustering-based methods face: introducing dependencies among
different tasks of the model. We find the key to its success is assigning a
suitable target to each sampled point. Instead of the commonly used static or
distance-based assignment strategies, we propose to use an Optimal Transport
approach to optimally assign target masks to the sampled points according to
the dynamic matching costs. Our approach achieves promising results on both
ScanNet and S3DIS benchmarks. The proposed approach removes intertask
dependencies and thus represents a simpler and more flexible 3D instance
segmentation framework than other competing methods, while achieving improved
segmentation accuracy.

该研究论文提出了一种全卷积 3D 点云实例分割方法，采用逐点预测方式，避免聚类方法中的任务依赖问题，并使用 Optimal Transport 方法根据动态匹配成本为每个采样点分配目标掩模，显著提升了场景的分割精度，成为比其他方法更简单灵活的 3D 实例分割框架。

PointInst3D：通过点对三维实例进行分割

PointInst3D: Segmenting 3D Instances by Points

Previous cycle-consistency correspondence learning methods usually leverage
image patches for training. In this paper, we present a fully convolutional
method, which is simpler and more coherent to the inference process. While
directly applying fully convolutional training results in model collapse, we
study the underline reason behind this collapse phenomenon, indicating that the
absolute positions of pixels provide a shortcut to easily accomplish
cycle-consistence, which hinders the learning of meaningful visual
representations. To break this absolute position shortcut, we propose to apply
different crops for forward and backward frames, and adopt feature warping to
establish correspondence between two crops of a same frame. The former
technique enforces the corresponding pixels at forward and back tracks to have
different absolute positions, and the latter effectively blocks the shortcuts
going between forward and back tracks. In three label propagation benchmarks
for pose tracking, face landmark tracking and video object segmentation, our
method largely improves the results of vanilla fully convolutional
cycle-consistency method, achieving very competitive performance compared with
the self-supervised state-of-the-art approaches. Our trained model and code are
available at https://github.com/Steve-Tod/STFC3.

本文提出了一种全卷积的方法，采用前向和后向帧之间的不同裁剪策略、特征变形等技术，破解绝对位置快捷方式，有效地阻止了前向和后向跟踪之间的快捷方式，并在三个标签传播基准测试中实现很好的性能。