Summary of Vertical Validation: Evaluating Implicit Generative Models For Graphs on Thin Support Regions, by Mai Elkady et al.
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions
by Mai Elkady, Thu Bui, Bruno Ribeiro, David I. Inouye
First submitted to arxiv on: 20 Nov 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 paper introduces Vertical Validation (VV), a novel evaluation method for implicit graph generative models designed to address the mismatch between traditional evaluation methods and the goal of generating novel molecules. By creating thin support regions during train-test splitting, VV allows generated samples to be compared to held-out test data, enabling model selection and better detection of overfitting. The approach is demonstrated on a task of molecule generation for medicine or material design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops an innovative way to check how well computers can create new molecules that don’t exist yet. Right now, we’re really good at finding old ones! To make sure our computer-generated molecules are actually new and useful, the authors came up with a clever method called Vertical Validation (VV). It’s like a special filter that makes sure the computer-generated molecules are compared to real ones we haven’t seen before. This helps us pick the best computers for generating new molecules and catch when they’re getting too good at just memorizing old ones. |
Keywords
» Artificial intelligence » Overfitting
