Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912902917652480 |
|---|---|
| author | Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie |
| author_facet | Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie |
| contents | This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling capabilities, they are typically trained offline and remain static during deployment. Our method enables online adaptation by continuously updating model parameters in response to incoming data. We evaluate our approach for linear-recurrent-unit SSMs using a small carbon emission dataset collected from embedded automotive hardware. Experimental results show that our method consistently reduces prediction error online during inference, demonstrating its potential for dynamic, resource-constrained environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00804 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie Computational Engineering, Finance, and Science Machine Learning Systems and Control This paper introduces a new approach for fine-tuning the predictions of structured state space models (SSMs) at inference time using real-time recurrent learning. While SSMs are known for their efficiency and long-range modeling capabilities, they are typically trained offline and remain static during deployment. Our method enables online adaptation by continuously updating model parameters in response to incoming data. We evaluate our approach for linear-recurrent-unit SSMs using a small carbon emission dataset collected from embedded automotive hardware. Experimental results show that our method consistently reduces prediction error online during inference, demonstrating its potential for dynamic, resource-constrained environments. |
| title | Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models |
| topic | Computational Engineering, Finance, and Science Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2508.00804 |