Parameter-Efficient Neural CDEs via Implicit Function Jacobians
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918261736603648 |
|---|---|
| author | Kuleshov, Ilya Zaytsev, Alexey |
| author_facet | Kuleshov, Ilya Zaytsev, Alexey |
| contents | Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawbacks, the main one being the number of parameters, required for the method's operation. In this paper, we propose an alternative, parameter-efficient look at Neural CDEs. It requires much fewer parameters, while also presenting a very logical analogy as the "Continuous RNN", which the Neural CDEs aspire to. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parameter-Efficient Neural CDEs via Implicit Function Jacobians Kuleshov, Ilya Zaytsev, Alexey Machine Learning Artificial Intelligence Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawbacks, the main one being the number of parameters, required for the method's operation. In this paper, we propose an alternative, parameter-efficient look at Neural CDEs. It requires much fewer parameters, while also presenting a very logical analogy as the "Continuous RNN", which the Neural CDEs aspire to. |
| title | Parameter-Efficient Neural CDEs via Implicit Function Jacobians |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.20625 |