Summary of Function-space Parameterization Of Neural Networks For Sequential Learning, by Aidan Scannell et al.
Function-space Parameterization of Neural Networks for Sequential Learning
by Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 technique converts neural networks from weight space to function space, allowing for scalability and handling of rich inputs like images. It achieves this through a dual parameterization that retains prior knowledge when past data is limited and incorporates new data efficiently. The approach demonstrates its strengths in uncertainty quantification and guiding exploration in model-based RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re learning something new every day. That’s the idea behind this research, which makes it easier to learn and remember things over time. The technique works by looking at how functions (like images) are connected, rather than just focusing on the individual weights of a neural network. This helps the network scale up to big datasets, retain what it learned before, and quickly adapt to new information. It even does well with tasks that require uncertainty and exploration. |
Keywords
* Artificial intelligence * Neural network
