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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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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
Evidence
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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 1 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.

afffundunlabeled
A Fast Learning Algorithm for Deep Belief Nets
Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh
2006· article· en· Neural Computation· Computer Science
machine prediction:candidate · noneconsensus · none
16,404
citations
affunlabeled
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
2009· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
3,486
citations
affunlabeled
A survey on semi-supervised learning
Jesper E. van Engelen, Holger H. Hoos
2019· article· en· Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
2,562
citations
affunlabeled
Optimization as a Model for Few-Shot Learning
Sachin Ravi, Hugo Larochelle
2017· article· en· International Conference on Learning Representations· Computer Science
machine prediction:candidate · noneconsensus · none
2,447
citations
affunlabeled
Zero-Shot Learning with Semantic Output Codes
Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell
2018· article· en· Figshare· Computer Science
machine prediction:candidate · noneconsensus · none
829
citations
affunlabeled
Deep learning for AI
Yoshua Bengio, Yann LeCun, Geoffrey E. Hinton
2021· article· en· Communications of the ACM· Computer Science
machine prediction:candidate · noneconsensus · none
627
citations
affunlabeled
A Review of Generalized Zero-Shot Learning Methods
Farhad Pourpanah, Moloud Abdar, Yuxuan Luo, Xinlei Zhou, Ran Wang, Chee Peng Lim +2 more
2022· review· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
396
citations
affunlabeled
MetaGAN: an adversarial approach to few-shot learning
Ruixiang Zhang, Tong Che, Zoubin Ghahramani, Yoshua Bengio, Yangqiu Song
2018· article· en· Cambridge University Engineering Department Publications Database· Computer Science
machine prediction:candidate · noneconsensus · none
283
citations
affunlabeled
Online Continual Learning with Maximal Interfered Retrieval
Rahaf Aljundi, Eugene Belilovsky, Tinne Tuytelaars, Laurent Charlin, M. Caccia, Min Lin +1 more
2019· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
206
citations
affunlabeled
Bayesian Model-Agnostic Meta-Learning
Jaesik Yoon, Taesup Kim, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
204
citations
affno abstractunlabeled
Meta-Learning for Semi-Supervised Few-Shot Classification
Eleni Triantafillou, Hugo Larochelle, Jake Snell, Josh Tenenbaum, Kevin Swersky, Mengye Ren +2 more
2018· article· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
203
citations
afffundunlabeled
Domain Adaptation With Neural Embedding Matching
Zengmao Wang, Bo Du, Yuhong Guo
2019· article· en· IEEE Transactions on Neural Networks and Learning Systems· Computer Science
machine prediction:candidate · noneconsensus · none
180
citations
affunlabeled
Ranking Distillation
Jiaxi Tang, Ke Wang
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
165
citations
affunlabeled
k-Sparse Autoencoders
Alireza Makhzani, Brendan J. Frey
2013· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
148
citations
affunlabeled
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
2018· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
129
citations
afffundunlabeled
Loss of plasticity in deep continual learning
Shibhansh Dohare, Juan Hernandez-Garcia, Qingfeng Lan, Parash Rahman, Ashique Rupam Mahmood, Richard S. Sutton
2024· article· en· Nature· Computer Science
machine prediction:candidate · noneconsensus · none
117
citations
fundno affunlabeled
When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Michael J. Wilber, Oisin Mac Aodha, Serge Belongie
2022· article· en· 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)· Computer Science
machine prediction:candidate · noneconsensus · none
98
citations
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