MétaCan
Menu
Cohort builder

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.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
International Conference on Learning Representations
Topic
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

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.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

80 results · 1 filter active ·
Results by year
20132021
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
80 works in the cohort · of 4,299,418page 2 of 2

Labels cover 0 of 80 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 80 of 80 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning.
Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher, Boris N. Oreshkin, Julien Cornebise
2019· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
4
citations
affunlabeled
DOM-Q-NET: Grounded RL on Structured Language
Sheng Jia, Jamie Kiros, Jimmy Ba
2019· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · insufficient_payload
3
citations
affno abstractunlabeled
Jointly Learning "What" and "How" from Instructions and Goal-States.
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette
2018· article· en· International Conference on Learning Representations· Decision Sciences
distilled prediction:candidate · scholarly_communication+insufficient_payloadconsensus · none
3
citations
affunlabeled
Decoupling the Layers in Residual Networks
Ricky Fok, Aijun An, Zana Rashidi, Xiaogang Wang
2018· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Recurrent Normalization Propagation
César Laurent, Nicolas Ballas, Pascal Vincent
2017· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affunlabeled
SELF-INFORMED NEURAL NETWORK STRUCTURE LEARNING
David Warde-Farley, Andrew Rabinovich, Dragomir Anguelov
2015· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Reconstructing evolutionary trajectories of mutations in cancer
Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit G. Deshwar, Nil Sahin +1 more
2018· article· en· International Conference on Learning Representations· Biochemistry, Genetics and Molecular Biology
distilled prediction:candidate · noneconsensus · none
1
citations
affunlabeled
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li +1 more
2021· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Online variance-reducing optimization
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
2018· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
How Chaotic Are Recurrent Neural Networks
Pourya Vakilipourtakalou, Lili Mou
2020· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
I❤LA: Compilable Markdown for Linear Algebra
Yong Li, Shoaib Kamil, Alec Jacobson, Yotam Gingold
2021· article· en· International Conference on Learning Representations· Computer Science
distilled prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About