两篇新的arXiv论文引入了使用Aitchison几何的表示学习新方法。一篇论文提出了一种通过将高能物理中的风味标记器校准构建为概率单纯形上的最优传输问题来校准风味标记器的框架。另一篇论文提出了一个成分图嵌入框架，该框架利用Aitchison几何为节点分类和链接预测等图机器学习任务创建可解释的嵌入。 AI
影响 这些论文提供了新的几何框架，以提高图机器学习和物理数据分析的可解释性和性能。
排序理由 两篇arXiv论文引入了使用Aitchison几何的表示学习新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2605.01363v1 Announce Type: cross Abstract: Flavor-tagging calibrations are often provided either as scale factors measured at a finite set of working points or as binned corrections to a chosen one-dimensional discriminant. However, this approach falls short of providing c…
arXiv:2605.00716v1 Announce Type: new Abstract: Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learned features r…
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret, offering limited insight into how learned features relate to graph structure. Many networks naturall…