Summary of Generative Principal Component Regression Via Variational Inference, by Austin Talbot et al.
Generative Principal Component Regression via Variational Inference
by Austin Talbot, Corey J Keller, David E Carlson, Alex V Kotlar
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel approach to designing stimulation targets for manipulating complex systems, such as the brain, with specific outcomes in mind. Generative latent variable models like probabilistic principal component analysis (PPCA) are powerful tools but struggle incorporating information relevant to low-variance outcomes into the latent space. To address this issue, the authors develop a supervised variational autoencoder (SVAE) objective that enforces representation of such information in the latent space. This novel approach can be used with linear models like PPCA, referred to as generative principal component regression (gPCR). The paper shows through simulations that gPCR dramatically improves target selection for manipulation compared to standard PCR and SVAEs. Additionally, a metric is developed to detect when relevant information is not properly incorporated into the loadings. The results are then demonstrated on two neural datasets related to stress and social behavior, where gPCR outperforms PCR in predictive performance and SVAEs exhibit low incorporation of relevant information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to manipulate complex systems like the brain to achieve specific outcomes. Right now, it’s hard to design good targets because current models don’t take into account certain types of information. The authors come up with a new way to make these models better by using something called supervised variational autoencoders (SVAEs). They show that this new approach works much better than the old methods and can be used to predict things like stress levels or social behavior. |
Keywords
» Artificial intelligence » Latent space » Principal component analysis » Regression » Supervised » Variational autoencoder
