摘要
Inspired by the Kolmogorov-Arnold representation theorem（表示定理）, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives（替代品） to Multi-Layer Perceptrons（多层感知机） (MLPs). While MLPs have fixed activation functions（固有的激活函数） on nodes（节点） (“neurons”), KANs have learnable activation functions（可学习的激活的函数） on edges（边缘） (“weights”). KANs have no linear weights at all（没有线性权重） – every weight parameter（权重参数） is replaced（取代） by a univariate function parametrized as a spline（参数化为样条的单变量函数）. We show that this seemingly simple change makes KANs outperform（优于） MLPs in terms of accuracy（准确性） and interpretability（可解释性）, on small-scale AI + Science tasks. For accuracy（在精度方面）, smaller KANs can achieve comparable or better accuracy（达到相当或者更好的准确度ÿ
