Distributed Associative Memory via Online Convex Optimization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914501253660672 |
|---|---|
| author | Wang, Bowen Zecchin, Matteo Simeone, Osvaldo |
| author_facet | Wang, Bowen Zecchin, Matteo Simeone, Osvaldo |
| contents | An associative memory (AM) enables cue-response recall, and associative memorization has recently been noted to underlie the operation of modern neural architectures such as Transformers. This work addresses a distributed setting where agents maintain a local AM to recall their own associations as well as selective information from others. Specifically, we introduce a distributed online gradient descent method that optimizes local AMs at different agents through communication over routing trees. Our theoretical analysis establishes sublinear regret guarantees, and experiments demonstrate that the proposed protocol consistently outperforms existing online optimization baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Associative Memory via Online Convex Optimization Wang, Bowen Zecchin, Matteo Simeone, Osvaldo Machine Learning Signal Processing An associative memory (AM) enables cue-response recall, and associative memorization has recently been noted to underlie the operation of modern neural architectures such as Transformers. This work addresses a distributed setting where agents maintain a local AM to recall their own associations as well as selective information from others. Specifically, we introduce a distributed online gradient descent method that optimizes local AMs at different agents through communication over routing trees. Our theoretical analysis establishes sublinear regret guarantees, and experiments demonstrate that the proposed protocol consistently outperforms existing online optimization baselines. |
| title | Distributed Associative Memory via Online Convex Optimization |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2509.22321 |