Summary of Informed Spectral Normalized Gaussian Processes For Trajectory Prediction, by Christian Schlauch et al.
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction
by Christian Schlauch, Christian Wirth, Nadja Klein
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed regularization-based continual learning method for spectral normalized Gaussian processes (SNGPs) enables the use of informative priors that represent prior knowledge learned from previous tasks. By integrating prior drivability knowledge, the informed SNGP model improves data-efficiency and robustness to location-transfers in trajectory prediction problems in autonomous driving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make machine learning models more efficient and accurate by using prior knowledge learned from previous tasks. This is achieved through a novel regularization-based continual learning method for spectral normalized Gaussian processes (SNGPs). The model is applied to the problem of predicting trajectories in autonomous driving, where it improves performance and robustness compared to non-informed and informed baselines. |
Keywords
* Artificial intelligence * Continual learning * Machine learning * Regularization
