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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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Reinforcement Learning in Robotics
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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.

1,145 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.
1,145 works in the cohort · of 4,299,418page 4 of 23

Labels cover 2 of 1,145 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 1,145 of 1,145 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
True Online TD(λ)
Richard S. Sutton
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
Improvised Theatre Alongside Artificial Intelligences
Kory W. Mathewson, Piotr Mirowski
2017· article· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Computer Science
machine prediction:candidate · noneconsensus · none
32
citations
affunlabeled
Monte-Carlo Tree Search for Constrained POMDPs
Jongmin Lee, Geon-Hyeong Kim, Pascal Poupart, Kee-Eung Kim
2018· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
31
citations
affunlabeled
Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
afffundunlabeled
Using Bisimulation for Policy Transfer in MDPs
Pablo F. Castro, Doina Precup
2010· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
27
citations
affunlabeled
Iterative Value-Aware Model Learning
Amir‐massoud Farahmand
2018· article· en· PolyPublie (École Polytechnique de Montréal)· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Universal Option Models
Hengshuai Yao, Csaba Szepesvári, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
2014· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
affunlabeled
Reinforcement Learning
2023· book-chapter· en· Cambridge University Press eBooks· Computer Science
machine prediction:candidate · noneconsensus · none
24
citations
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