Summary of Mapping the Multiverse Of Latent Representations, by Jeremy Wayland et al.
Mapping the Multiverse of Latent Representations
by Jeremy Wayland, Corinna Coupette, Bastian Rieck
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Algebraic Topology (math.AT); Machine Learning (stat.ML)
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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 This paper proposes a framework called PRESTO that tackles the issue of unreliable machine learning model embeddings by analyzing the multiverse of models relying on latent representations. The authors highlight how widespread adoption of these models has led to uncertainty about their variability, resulting in complexity and untrustworthy representations. To address this, they develop a principled framework using persistent homology to characterize latent spaces generated by different combinations of machine learning methods, hyperparameter configurations, and datasets. This allows for measuring pairwise similarity and statistically reasoning about distributions. The paper demonstrates both theoretically and empirically that the pipeline preserves desirable properties of collections of latent representations, and it can be applied for sensitivity analysis, detecting anomalous embeddings, or efficiently navigating hyperparameter search spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors create a framework called PRESTO to help with machine learning model reliability. They look at how different models work together and what they mean when they use “latent representations.” This is important because it can help us understand how our models are working and make them better. The framework uses special math tools to figure out how similar or different these models are. It also helps us find problems with the models and make them better. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning
