Summary of Comparing the Information Content Of Probabilistic Representation Spaces, by Kieran A. Murphy et al.
Comparing the information content of probabilistic representation spaces
by Kieran A. Murphy, Sam Dillavou, Dani S. Bassett
First submitted to arxiv on: 31 May 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 In this paper, researchers aim to bridge the gap in comparing probabilistic representation spaces, which are crucial for understanding the learning process. The traditional methods assume point-based representations, neglecting the distributional nature of these spaces. To address this limitation, the authors propose two information-theoretic measures that extend classic methods for comparing the information content of hard clustering assignments to general probabilistic representation spaces. These measures include a lightweight estimation method based on fingerprinting a representation space with a sample of the dataset. The proposed approach is demonstrated in three case studies: unsupervised disentanglement, comparing full latent spaces of models across datasets and methods, and model fusion for synthesizing information content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers propose new ways to understand how information is processed by comparing probabilistic representation spaces. They want to know what a dataset looks like in different types of spaces, such as those created by different machine learning models or architectures. The authors show that their methods can be used in three important applications: separating information in complex datasets, understanding how different models work together, and combining the strengths of multiple models. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Unsupervised
