Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors

arXiv:2602.00315v2 Announce Type: replace-cross Abstract: How close are neural networks to the best they could possibly do? Standard benchmarks cannot answer this because they lack access to the true posterior p(y|x). We use class-conditional normalizing flows as oracles that ...

🔗 Read more: https://arxiv.org/abs/2602.00315

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