Uncertainty quantification with Bayesian higher order ReLU-KANs
We introduce the first method of uncertainty quantification in the domain of Kolmogorov–Arnold Networks, specifically focusing on (Higher Order) ReLU-KANs to enhance computational efficiency given the computational demands of Bayesian methods. The method we propose is general in nature, providing ac...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adbeb7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|