inlong-sort的入口在哪

inlong-sort是InLong（阿里巴巴集团的一款开源流式计算平台）中的一个排序组件，其入口可以在InLong的官方Github仓库中找到。具体来说，您可以在https://github.com/alibaba/InLong/tree/master/inlong-sort路径下找到inlong-sort的源代码、配置文件以及使用文档等相关内容。

相关问题

Exploring Classification Equilibrium in Long-Tailed Object Detection

"Exploring Classification Equilibrium in Long-Tailed Object Detection" is a research paper that discusses the problem of long-tailed distribution in object detection tasks. Long-tailed distribution means that there are many classes with very few samples, while a few classes have a large number of samples. The paper proposes a method called Class-Balanced Loss with Dynamic Scaled Sampling (CB-DSS) to address this problem. CB-DSS involves re-balancing the loss function and sampling strategy to give more importance to underrepresented classes. The paper also explores the concept of classification equilibrium, where the classifier is trained to maintain a balance between the accuracy of rare and frequent classes. The proposed method is evaluated on multiple datasets and is shown to outperform existing state-of-the-art methods in long-tailed object detection tasks.

long-tail distribution

Long-tail distribution是指数据集或样本中出现少量标签或类别拥有大多数样本，而大量标签或类别只拥有很少样本的情况。在图数据中，long-tail distribution问题主要指的是节点度分布不均衡，即一些节点拥有非常高的度数，而其他节点的度数相对较低。这种不均衡分布会导致模型对于度数较低的节点关注度不高，从而在tail-node set上表现不佳。

这种问题在图数据中与常规欧式数据上的long-tail distribution问题有所不同。常规欧式数据的long-tail distribution问题主要指的是样本标签不平衡，少量的标签拥有大多数的样本，而大量的标签只有很少的样本。针对这种问题，目前的研究主要思路是通过重新采样和成本敏感学习来解决，其中包括一些解决方案。

在图数据中，解决long-tail distribution问题的方法还需要考虑节点度分布的不均衡。有关这个问题的具体解决方法还需要进一步研究和探索。但是长尾分布的存在与二次供应和需求有关，它驱动了Long Tail在线的发展。此外，尽管幂律是等级销售关系的一个良好的近似值，但对于所有的节点度，斜率并不是恒定的。<span class="em">1</span><span class="em">2</span><span class="em">3</span><span class="em">4</span>