시벤코 정리

노트

그것이 오늘 말씀드릴 Universal Approximation Theorem, UAT 입니다.^[1]
Our result can be viewed as a universal approximation theorem for MoE models.^[2]
A variant of the universal approximation theorem was proved for the arbitrary depth case by Zhou Lu et al.^[3]
One may be inclined to point out that the Universal Approximation Theorem, simple as it is, is a little bit too simple (the concept, at least).^[4]
Of course, the Universal Approximation Theorem assumes that one can afford to continue adding neurons on to infinity, which is not feasible in practice.^[4]
Does a linear function suffice at approaching the Universal Approximation Theorem?^[5]
In this paper, we prove the universal approximation theorem for such interval NN's.^[6]
The classical Universal Approximation Theorem holds for neural networks of arbitrary width and bounded depth.^[7]
This universal approximation theorem of operators is suggestive of the potential of NNs in learning from scattered data any continuous operator or complex system.^[8]
I think it’s best not to get too hung up on this Universal Approximation Theorem.^[9]
In this post, we will talk about the Universal approximation theorem and we will also prove the theorem graphically.^[10]