Summary of Deep Nonparametric Conditional Independence Tests For Images, by Marco Simnacher et al.
Deep Nonparametric Conditional Independence Tests for Images
by Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Statistics Theory (math.ST); Methodology (stat.ME)
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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 deep nonparametric conditional independence tests (DNCITs) to analyze complex high-dimensional variables like images. The approach combines embedding maps that extract feature representations with nonparametric CITs applicable to these features. The authors derive general properties for the embedding map estimators and show that they include those learned through unsupervised or transfer learning. They also select and adapt nonparametric CITs for use with feature representations. Simulations demonstrate the performance of DNCITs under varying conditions, including confounder dimensions and relationships. The approach is applied to brain MRI scans and behavioral traits from the UK Biobank, confirming null results from previous studies. Additionally, the paper presents a study on confounder control using brain MRI scans and a confounder set, which suggests a potential reduction in confounder dimension under improved control. The authors provide an R package implementing the DNCITs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores new ways to test for relationships between different things (like images) while controlling for other factors that might affect the results. This is important because it helps us understand how different things are connected and what might be causing certain patterns or behaviors. The authors create a special kind of test that can handle really complex data like brain scans and personality traits. They use this test to look at some old studies and find out that they didn’t actually show any real connections between the things being studied. They also do a new study using their test and find that it’s possible to reduce the number of factors we need to control for if we’re careful about how we collect our data. |
Keywords
» Artificial intelligence » Embedding » Transfer learning » Unsupervised
