Summary of A Theoretical Perspective on Mode Collapse in Variational Inference, by Roman Soletskyi et al.
A theoretical perspective on mode collapse in variational inference
by Roman Soletskyi, Marylou Gabrié, Bruno Loureiro
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 delves into the limitations of traditional variational inference methods for deep learning, specifically the minimization of the Kullback-Leibler objective. It explores the phenomenon of mode collapse, where models focus on a few modes of the target distribution despite being capable of expressing them all. The authors conduct a theoretical investigation of mode collapse on Gaussian mixture models, identifying key low-dimensional statistics and deriving closed equations governing their evolution. They find that mode collapse is present even in favorable scenarios, driven by mean alignment and vanishing weights. This work offers practical insights for variational inference using normalizing flows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep learning can be used to make more complex models for things like pictures or sounds. One problem with these models is that they sometimes get stuck on just a few ways of making things, instead of being able to make lots of different things. The authors want to understand why this happens and find ways to fix it. They do some math to figure out what’s going wrong and find two main reasons: the model gets too good at one way of making things, or the model doesn’t use its powers very well. This research can help make better models for things like pictures or sounds. |
Keywords
» Artificial intelligence » Alignment » Deep learning » Inference
