Summary of Non-stationary Learning Of Neural Networks with Automatic Soft Parameter Reset, by Alexandre Galashov et al.
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
by Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
First submitted to arxiv on: 6 Nov 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 method introduces a novel learning approach that adapts to non-stationarity by modeling an Ornstein-Uhlenbeck process with an adaptive drift parameter. This soft parameter reset technique is shown to perform well in non-stationary supervised and off-policy reinforcement learning settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to learn when the data distribution changes over time. By using an Ornstein-Uhlenbeck process, the approach can adapt to these changes and avoid getting stuck in old patterns. The method is tested in different scenarios, including when the data is no longer coming from the same place. |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised
