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concept:neural-collapseNeural Collapse
Terminal phase phenomenon in deep learning training relevant to convergence of representations
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- Input-InjectivitycontradictsAssumption that DNN layers preserve input information by being injective; key condition for Theorem 1
Related by similarity (8)
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.
- Threshold between ordered and chaotic dynamics; predicted to be more prevalent in post-dual agents due to lower VFE
- The model's parameters considered as the actual 'code' implementing its algorithms, as opposed to human-written code.
- Cognition in nervous systems, used as a modelling target
- Michael Johnson's prior work on how neural networks (and brains) can be 'annealed' to find optimal states.
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