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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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In Figure 3 with 50,000 MNIST training examples, AEVB has the highest displayed train and test marginal-likelihood curves, wake-sleep is lower, and MCEM improves much later and remains lower over the plotted training range. · CiteArk

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In Figure 3 with 50,000 MNIST training examples, AEVB has the highest displayed train and test marginal-likelihood curves, wake-sleep is lower, and MCEM improves much later and remains lower over the plotted training range.

Source: source-paper:PDF pp. 7-8; Section 5 'Marginal likelihood' and Figure 3, right panel (N_train=50000)

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Runs (0)

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Research Plan identity

Claim version: sha256:2df9aef07818690b4146972d66699ae666d4483d91ac51dd9ccd75e8e4747063
Planned experiments: 1
Evidence objects: 0

The 50000-example Figure 3 package preserves AEVB, wake-sleep and the delayed MCEM trajectory over the displayed range and evaluates the reported ordering as a declared qualitative relation.

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