Summary of Intrinsic Dimension Correlation: Uncovering Nonlinear Connections in Multimodal Representations, by Lorenzo Basile et al.
Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations
by Lorenzo Basile, Santiago Acevedo, Luca Bortolussi, Fabio Anselmi, Alex Rodriguez
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 metric quantifies the potentially nonlinear correlation between high-dimensional manifolds by exploiting entanglement with intrinsic dimensionality. Standard methods struggle to detect correlations due to their nonlinear and high-dimensional nature. This paper validates the method on synthetic data, showcasing advantages and drawbacks compared to existing techniques. It then extends the analysis to neural network representations of multimodal data, revealing clear correlations between visual and textual embeddings that existing methods struggle to detect. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to understand how machine learning works by looking at connections between features in data points. This is hard because these connections can be complex and high-dimensional. The researchers propose a method to measure these connections and test it on fake data to see how well it works. Then, they use this method to look at real data from neural networks that process text and images. They find that there are strong connections between the two types of data that other methods can’t detect. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Synthetic data
