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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Generative Adversarial Networks and Image Synthesis
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

668 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
668 works in the cohort · of 4,299,418page 4 of 14

Labels cover 1 of 668 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 668 of 668 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Learning to Learn with Compound HD Models
Antonio Torralba, Joshua B. Tenenbaum, Ruslan Salakhutdinov
2011· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
26
citations
affunlabeled
PixelGAN Autoencoders
Alireza Makhzani, Brendan J. Frey
2017· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
25
citations
affunlabeled
On autoencoder scoring
Hanna Kamyshanska, Roland Memisevic
2013· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Invertible Convolutional Flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl‐Dickstein, Laurent Dinh, Daniel Duckworth
2019· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Tails of Lipschitz Triangular Flows
Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Deep Convolutional Sum-Product Networks
Cory J. Butz, Jhonatan S. Oliveira, André E. dos Santos, André Teixeira
2019· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
18
citations

How this was built: Screen · Findings · About