Summary of Compositional Structures in Neural Embedding and Interaction Decompositions, by Matthew Trager et al.
Compositional Structures in Neural Embedding and Interaction Decompositions
by Matthew Trager, Alessandro Achille, Pramuditha Perera, Luca Zancato, Stefano Soatto
First submitted to arxiv on: 12 Jul 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 The abstract proposes a framework that connects linear algebraic structures in vector embeddings with conditional independence constraints on probability distributions modeled by artificial neural networks. This connection aims to provide a formal understanding of structural patterns emerging in data representations, a phenomenon widely acknowledged but lacking a solid foundation. The framework introduces “interaction decompositions” and establishes necessary and sufficient conditions for the presence of compositional structures within model representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how neural networks represent data by introducing a connection between linear algebraic structures and conditional independence constraints. This research aims to provide a formal understanding of structural patterns in data, which is an important but still not fully understood area. The framework uses “interaction decompositions” to characterize compositional structures and establish conditions for their presence in model representations. |
Keywords
* Artificial intelligence * Probability
