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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 2 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
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen
2019· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
193
citations
affunlabeled
Denoising Criterion for Variational Auto-Encoding Framework
Daniel Im Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
142
citations
affunlabeled
Multi-Prediction Deep Boltzmann Machines
Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
2013· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
116
citations
affunlabeled
Where Do Features Come From?
Geoffrey E. Hinton
2013· article· en· Cognitive Science· Computer Science
machine prediction:candidate · noneconsensus · none
109
citations
affunlabeled
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Joern-Henrik Jacobsen
2019· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
109
citations
affunlabeled
A Better Way to Pretrain Deep Boltzmann Machines
Geoffrey E. Hinton, Ruslan Salakhutdinov
2012· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
106
citations
affunlabeled
Better Mixing via Deep Representations
Yoshua Bengio, Grégoire Mesnil, Yann Dauphin, Salah Rifai
2012· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
104
citations
affunlabeled
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
97
citations
affunlabeled
State of the Art on Diffusion Models for Visual Computing
Riccardo Pó, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Edwin P. Chan +11 more
2024· article· en· Computer Graphics Forum· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
affunlabeled
Learning Generative Models with Visual Attention
Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
2013· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
86
citations
affunlabeled
Object-Centric Image Generation from Layouts
Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R Devon Hjelm, Shikhar Sharma
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
79
citations
affunlabeled
A Spike and Slab Restricted Boltzmann Machine
Aaron Courville, James Bergstra, Yoshua Bengio
2011· article· en· International Conference on Artificial Intelligence and Statistics· Computer Science
machine prediction:candidate · noneconsensus · none
77
citations

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