XBNet：一种增强型神经网络
XBNet : An Extremely Boosted Neural Network
摘要：
神经网络已被证明在处理图像、文本、视频和音频等非结构化数据方面非常强大。然而，已经观察到，它们的性能在表格数据中没有达到标准;因此，在这种情况下，基于树的模型是首选的。一种流行的表格数据模型是提升树，这是一种高效且广泛使用的机器学习方法，与神经网络相比，它还提供了良好的可解释性。在本文中，我们描述了一种新的架构XBNet，它试图将基于树的模型与神经网络相结合，以创建一个强大的架构，通过使用一种新的优化技术，增强梯度下降的表格数据，提高其可解释性和性能的训练。
英文摘要：
Neural networks have proved to be very robust at processing unstructured data like images, text, videos, and audio. However, it has been observed that their performance is not up to the mark in tabular data; hence tree-based models are preferred in such scenarios. A popular model for tabular data is boosted trees, a highly efficacious and extensively used machine learning method, and it also provides good interpretability compared to neural networks. In this paper, we describe a novel architecture XBNet, which tries to combine tree-based models with that of neural networks to create a robust architecture trained by using a novel optimization technique, Boosted Gradient Descent for Tabular Data which increases its interpretability and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
