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concept:yamins-et-al-2014Yamins et al. (2014)
Showed that performance-optimized neural networks align with biological brain representations in higher visual cortex
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cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Introduced the Contravariance principle; closely related theoretical foundation for multitask scaling hypothesis
- Showed contrastive learning inverts the data generating process; supports claim that contrastive learners recover statistics of underlying world
- Discovered that color distances in language representations mirror human perceptual distances; reproduced and extended in this paper
- Found Rosetta Neurons — neurons activated by same patterns across diverse vision models
- Recent ToM in AI work cited as emphasizing inference of others' beliefs, contrasted with this paper's self-focus
- Introduced CKA and observed that model alignment increases with model scale and dataset size
- Reference for ToM modeled through partially observable inference of others' beliefs
- Authors of foundational text on CPOs and ordered structures; referenced for theory developed since 1970s.