시벤코 정리

노트

위키데이터

• ID : Q7894110

말뭉치

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

메타데이터

위키데이터

• ID : Q7894110

Spacy 패턴 목록

• [{'LOWER': 'universal'}, {'LOWER': 'approximation'}, {'LEMMA': 'theorem'}]