  The neurons of Kolmogorov-Arnold Networks (KANs) perform a simple summation
motivated by the Kolmogorov-Arnold representation theorem, which asserts that
sum is the only fundamental multivariate function. In this work, we investigate
the potential for identifying an alternative multivariate function for KAN
neurons that may offer increased practical utility. Our empirical research
involves testing various multivariate functions in KAN neurons across a range
of benchmark Machine Learning tasks.
  Our findings indicate that substituting the sum with the average function in
KAN neurons results in significant performance enhancements compared to
traditional KANs. Our study demonstrates that this minor modification
contributes to the stability of training by confining the input to the spline
within the effective range of the activation function. Our implementation and
experiments are available at: \url{https://github.com/Ghaith81/dropkan}
