MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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
Topic
Neural Networks and Applications
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.

2,372 results · 1 filter active ·
Results by year
20002025
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.
2,372 works in the cohort · of 4,299,418page 7 of 48

Labels cover 1 of 2,372 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 2,372 of 2,372 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
Reversible Recurrent Neural Networks
Matthew Mackay, Paul Vicol, Jimmy Ba, Roger Grosse
2018· article· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · noneconsensus · none
21
citations
affno abstractunlabeled
Learning with hidden variables
Yasser Roudi, Graham W. Taylor
2015· review· en· Current Opinion in Neurobiology· Computer Science
distilled prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Archimedean-Compensatory Fuzzy Logic Systems
Rafael Alejandro Espín Andrade, Erick González Caballero, Witold Pedrycz, Eduardo René Fernández González
2015· article· en· International Journal of Computational Intelligence Systems· Computer Science
distilled prediction:candidate · noneconsensus · none
21
citations
affunlabeled
Neural-network quantum states
2025· book-chapter· en· Cambridge University Press eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
21
citations
affunlabeled
Machine learning for neuroscience
Geoffrey E. Hinton
2011· article· en· Neural Systems & Circuits· Computer Science
distilled prediction:candidate · noneconsensus · none
21
citations
afffundunlabeled
Pattern classification by assembling small neural networks
Liang Chen
2006· article· en· Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communicationconsensus · none
20
citations
afffundno abstractunlabeled
Predicting shifts in generalization gradients with perceptrons
Matthew G. Wisniewski, Milen L. Radell, Lauren M. Guillette, Christopher B. Sturdy, Eduardo Mercado
2011· article· en· Learning & Behavior· Computer Science
distilled prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Attentional Alignment Networks.
Lei Yue, Xin Miao, Pengbo Wang, Baochang Zhang, Xiantong Zhen, Xianbin Cao
2018· article· en· British Machine Vision Conference· Computer Science
distilled prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Introduction to artificial neural networks (ANN)
Ahmed Fawzy Gad, Fatima Ezzahra Jarmouni
2021· book-chapter· en· Elsevier eBooks· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
20
citations
affunlabeled
Factors of overtraining with fuzzy ARTMAP neural networks
Philippe Henniges, Éric Granger, Robert Sabourin
2006· article· en· Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
20
citations
affunlabeled
Hyperbolic tangent passive resistive-type neuron
Jafar Shamsi, Amirali Amirsoleimani, Sattar Mirzakuchaki, Arash Ahmade, Shahpour Alirezaee, Majid Ahmadi
2015· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Arithmetic for digital neural networks
David Zhang, G.A. Jullien, William C. Miller, Earl E. Swartzlander
2002· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
20
citations
affunlabeled
Mean-Field Networks
Yujia Li, Richard S. Zemel
2014· preprint· en· arXiv (Cornell University)· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
19
citations
affno abstractunlabeled
Deep Learning Vector Quantization.
Harm de Vries, Roland Memisevic, Aaron Courville
2016· article· en· The European Symposium on Artificial Neural Networks· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training
Haochen Sun, Tonghe Bai, J. Li, Change Institutions to: University of Waterloo
2024· article· en· IEEE Transactions on Information Forensics and Security· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
← previousnext →

How this was built: Screen · Findings · About