Report
Diffusion-Augmented Neural Processes
| العنوان: | Diffusion-Augmented Neural Processes |
|---|---|
| المؤلفون: | Bonito, Lorenzo, Requeima, James, Shysheya, Aliaksandra, Turner, Richard E. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, I.2.6 |
| الوصف: | Over the last few years, Neural Processes have become a useful modelling tool in many application areas, such as healthcare and climate sciences, in which data are scarce and prediction uncertainty estimates are indispensable. However, the current state of the art in the field (AR CNPs; Bruinsma et al., 2023) presents a few issues that prevent its widespread deployment. This work proposes an alternative, diffusion-based approach to NPs which, through conditioning on noised datasets, addresses many of these limitations, whilst also exceeding SOTA performance. Comment: Accepted to the NeurIPS 2023 Workshop on Diffusion Models |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.09848 |
| رقم الانضمام: | edsarx.2311.09848 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |