Kaydedildi:
| Yazar: | |
|---|---|
| Materyal Türü: | Recurso digital |
| Dil: | |
| Baskı/Yayın Bilgisi: |
Zenodo
2026
|
| Konular: | |
| Online Erişim: | https://doi.org/10.5281/zenodo.19026804 |
| Etiketler: |
Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
|
İçindekiler:
- This paper investigates the application of proximal gradient methods to optimization problems arising in scenarios with inherent uncertainty, particularly focusing on challenges posed by sparse data. We propose a modified proximal gradient algorithm incorporating a robust regularization term designed to mitigate the effects of noise and missing information. The efficacy of the proposed framework is demonstrated through a case study involving sparse behavioral data, drawing connections to recent advancements in representation learning.