Summary of Lars-vsa: a Vector Symbolic Architecture For Learning with Abstract Rules, by Mohamed Mejri et al.
LARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules
by Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 paper proposes a novel neuro-symbolic architecture that combines symbolic and connectionist approaches to enable learning from limited amounts of data. The architecture is inspired by the “relational bottleneck” strategy, which separates object-level features from abstract rules. However, this approach is vulnerable to the curse of compositionality, where similar object representations interfere with each other. To overcome this issue, the paper leverages hyperdimensional computing, which is inherently robust to interference, and adapts the relational bottleneck strategy to a high-dimensional space. Additionally, it designs a novel high-dimensional attention mechanism that utilizes relational representation. The system benefits from low overhead operations in hyperdimensional space, making it more efficient than state-of-the-art models while maintaining accuracy on various test datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for computers to learn and understand complex rules and patterns using limited data. It combines two different approaches, symbolic and connectionist, to help machines learn from small amounts of information. The system uses high-dimensional space and attention mechanisms to overcome the problem of similar representations interfering with each other. This innovation leads to more efficient and accurate results compared to current state-of-the-art models. |
Keywords
» Artificial intelligence » Attention
