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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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Domain Adaptation and Few-Shot Learning
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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.

705 results · 1 filter active ·
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20012025
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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.
705 works in the cohort · of 4,299,418page 2 of 15

Labels cover 0 of 705 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 705 of 705 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.

afffundno abstractunlabeled
Source-free domain adaptation for image segmentation
Mathilde Bateson, Hoel Kervadec, José Dolz, Hervé Lombaert, Ismail Ben Ayed
2022· article· en· Medical Image Analysis· Computer Science
machine prediction:candidate · noneconsensus · none
92
citations
affunlabeled
Learning Deep Parsimonious Representations
Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
2016· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
90
citations
affunlabeled
Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, Richard S. Zemel
2017· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
85
citations
affunlabeled
Diversity Transfer Network for Few-Shot Learning
Mengting Chen, Yuxin Fang, Xinggang Wang, Heng Luo, Yifeng Geng, Xinyu Zhang +3 more
2020· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
75
citations
fundno affunlabeled
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
2020· article· en· Data Archiving and Networked Services (DANS)· Computer Science
machine prediction:candidate · noneconsensus · none
70
citations
fundno affunlabeled
Contrastive Learning of Structured World Models
Thomas Kipf, Elise van der Pol, Max Welling
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
affno abstractunlabeled
Deep Learning of Representations
Yoshua Bengio, Aaron Courville
2013· book-chapter· en· Intelligent systems reference library· Computer Science
machine prediction:candidate · noneconsensus · none
63
citations
affunlabeled
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
Inkyu Shin, Yi–Hsuan Tsai, Bingbing Zhuang, Samuel Schulter, Buyu Liu, Sparsh Garg +2 more
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
58
citations
affunlabeled
Laplacian Regularized Few-Shot Learning
Imtiaz Masud Ziko, José Dolz, Éric Granger, Ismail Ben Ayed
2020· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
56
citations
affunlabeled
Environment Inference for Invariant Learning
Elliot Creager, Joern-Henrik Jacobsen, Richard S. Zemel
2021· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
51
citations
affno abstractunlabeled
Information Maximization for Few-Shot Learning
Malik Boudiaf, Imtiaz Masud Ziko, Jérôme Rony, José Dolz, Pablo Piantanida, Ismail Ben Ayed
2020· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
2018· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
49
citations
affunlabeled
Few-shot Learning with Noisy Labels
Kevin J Liang, Samrudhdhi B. Rangrej, Vladan Petrović, Tal Hassner
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
46
citations

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