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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.

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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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

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 10 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 teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Inverse mapping of face GANs
Nicky Bayat, Vahid Reza Khazaie, Yalda Mohsenzadeh
2020· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affunlabeled
Symmetric Wasserstein Autoencoders
Sun Sun, Hongyu Guo
2021· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
1
citations
affunlabeled
Uncertainty in Neural Processes
Saeid Naderiparizi, Kenny Chiu, Benjamin Bloem-Reddy, Frank Wood
2020· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
affunlabeled
TzK: Flow-Based Conditional Generative Model
Micha Livne, David J. Fleet
2019· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
affunlabeled
Learning to sample better
Michael S. Albergo, Eric Vanden‐Eijnden
2024· article· en· Journal of Statistical Mechanics Theory and Experiment· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
fundno affunlabeled
Deep learning-based image outpainting of finger-vein image
Jun Tae Kim, Jin Seong Hong, Jung Soo Kim, Seong In Jeong, Seok Jun Lim, Won Ho Jang +1 more
2025· article· en· Expert Systems with Applications· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Learning enhanced ensemble filters
Eviatar Bach, Ricardo Baptista, Edoardo Calvello, Bohan Chen, Andrew M. Stuart
2025· article· en· Journal of Computational Physics· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Advanced deep learning methods
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher Pal, James R. Foulds
2025· book-chapter· en· Elsevier eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
0
citations
affunlabeled
Generative AI applied for synthetic data in PMU
Felipe Proença de Albuquerque, Eduardo C. Marques Costa, Luisa Helena Bartocci Liboni
2025· article· en· Energy Reports· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
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