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This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original KST formulation, while mathematically elegant, presents practical challenges due to its limited insight
Externí odkaz:
http://arxiv.org/abs/2410.01990
Autor:
Qadeer, Saad, Engel, Andrew, Howard, Amanda, Tsou, Adam, Vargas, Max, Stinis, Panos, Chiang, Tony
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitioners. This is partly due to the inability to determine the closed forms
Externí odkaz:
http://arxiv.org/abs/2310.18612
Publikováno v:
SIAM Journal on Numerical Analysis; Jan2024, Vol. 62 Issue 1, p1-24, 24p
Publikováno v:
SIAM Journal on Numerical Analysis; 2021, Vol. 59 Issue 3, p1811-1834, 24p