Summary of Tensor Network Square Root Kalman Filter For Online Gaussian Process Regression, by Clara Menzen and Manon Kok and Kim Batselier
Tensor network square root Kalman filter for online Gaussian process regression
by Clara Menzen, Manon Kok, Kim Batselier
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel approach called the tensor network square root Kalman filter to solve high-dimensional recursive estimation problems. This method addresses the curse of dimensionality by using tensor networks, but traditional rounding operations can cause divergence due to lost positive definiteness. The authors propose a solution and apply it to online Gaussian process regression tasks. Their experiments demonstrate that their approach outperforms existing methods in prediction accuracy and uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new method for high-dimensional recursive estimation problems, called the tensor network square root Kalman filter. This method can solve big problems with many variables, but it had a problem where the numbers didn’t add up right. The authors fixed this issue and tested their approach on real-life data to estimate millions of parameters on a regular laptop. Their results show that their method is better than others at predicting and understanding uncertainty. |
Keywords
» Artificial intelligence » Regression
