I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Recurso digital |
| Reo: | |
| I whakaputaina: |
Zenodo
2025
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| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.5281/zenodo.17385441 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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Rārangi ihirangi:
- <p><span>We have developed an efficient low-rank attention-augmented Gaussian processes (LAAGP) </span><span>model that effectively combines accuracy with a reduction in the computational costs associated with transformer attention and Gaussian processes (GP). This model addresses the limitations of standard GP models, such as poor covariance function expressiveness for long-range multivariate forecasting and inadequate </span><span>data representation capacity. LAAGP is a powerful forecasting technique that integrates the transformer </span><span>self-attention mechanism with GP. The framework features a transformer encoder that processes the input </span><span>embeddings to extract essential information, using positional and variable encoding along with relative </span><span>embeddings to enhance attention scores. The GP decoder, known for its flexibility and reliable uncertainty </span><span>estimates, has been adapted to predict the system’s evolution over time. This enhancement enables the model to achieve a balance between computational efficiency, predictive accuracy, and uncertainty quantification, thereby enhancing performance on complex tasks such as</span><span> long-range predictions. Our model has been evaluated </span><span>on several benchmark regression and classification datasets.</span></p>