In the news
Limits of Confidence in Diffusion
Apple Machine Learning Research · Published · 3 min read
In 30 seconds
- What happened
- Apple researchers identified fundamental limitations in discrete diffusion models where multi-position sampling steps violate training distributions due to token dependencies.
- Why it matters
- Engineers building or debugging discrete diffusion systems for images, speech, or text need to understand when per-position confidence scores fail to capture joint dependencies.
- Watch out
- Per-position marginal distributions alone cannot determine if token groups are dependent; identical marginals can mask different joint distributions, complicating model validation.
- token
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