Shortcut learning in geometric knot classification
几何结分类中的捷径学习
机构 * School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, UK(爱丁堡大学数学学院) ; School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, UK(爱丁堡大学物理与天文学学院) ; MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK(爱丁堡大学人类遗传学单位) ; International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM$^2$), Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan(广岛大学可持续性与结合手性超材料国际研究所)
AI总结 本文研究了机器学习在几何结分类中的捷径方法,揭示训练数据中的非拓扑特征,并提供公开数据集和代码以促进ML在该领域的应用。
Comments 17 pages, 6 figures, submitted to Machine Learning: Science and Technology, IOP