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

affno abstractunlabeled
S2match: Self-Paced Sampling for Data-Limited Semi-Supervised Learning
Dayan Guan, Yun Xing, Jiaxing Huang, Aoran Xiao, Abdulmotaleb El Saddik, Shijian Lu
2023· preprint· en· SSRN Electronic Journal· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+research_integrityconsensus · none
5
citations
affunlabeled
Adaptive Cross-Modal Few-Shot Learning
Xing Chen, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro
2019· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
5
citations
affunlabeled
Policy Search with High-Dimensional Context Variables
Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Interactive Transfer Learning in Relational Domains
Raksha Kumaraswamy, Nandini Ramanan, Phillip Odom, Sriraam Natarajan
2020· article· en· KI - Künstliche Intelligenz· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
5
citations
affunlabeled
Fast Cross-Validation for Incremental Learning
Pooria Joulani, András György, Csaba Szepesvári
2015· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
5
citations
affunlabeled
The Effect of Diversity in Meta-Learning
Ramnath Kumar, Tristan Deleu, Yoshua Bengio
2023· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Latent Attention Augmentation for Robust Autonomous Driving Policies
Ran Cheng, Christopher Agia, Florian Shkurti, David Meger, Gregory Dudek
2021· article· en· 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
5
citations
affunlabeled
Attentive Task Interaction Network for Multi-Task Learning
Dimitrios Sinodinos, Narges Armanfard
2022· article· en· 2022 26th International Conference on Pattern Recognition (ICPR)· Computer Science
distilled prediction:candidate · metaepi_narrow+insufficient_payloadconsensus · none
5
citations
affunlabeled
Data-driven deep density estimation
Patrik Puchert, Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
2021· article· en· Neural Computing and Applications· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Transfer Learning With Reconstruction Loss
Wei Cui, Wei Yu
2024· article· en· IEEE Transactions on Machine Learning in Communications and Networking· Computer Science
distilled prediction:candidate · noneconsensus · none
4
citations
afffundunlabeled
Pulling Up by the Causal Bootstraps
Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi
2021· preprint· en· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
4
citations

How this was built: Screen · Findings · About