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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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Advanced Neural Network Applications
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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.

1,336 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.
1,336 works in the cohort · of 4,299,418page 3 of 27

Labels cover 0 of 1,336 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 1,336 of 1,336 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
Bit-Tactical
Alberto Delmás Lascorz, Patrick Judd, Dylan Malone Stuart, Zissis Poulos, Mostafa Mahmoud, Sayeh Sharify +3 more
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
107
citations
fundno affunlabeled
Understanding Mixup Training Methods
Daojun Liang, Feng Yang, Tian Zhang, Peter Yang
2018· article· en· IEEE Access· Computer Science
machine prediction:candidate · noneconsensus · none
105
citations
affunlabeled
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
2019· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
104
citations
affunlabeled
Fast AutoAugment.
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim
2019· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
95
citations
affunlabeled
Laconic deep learning inference acceleration
Sayeh Sharify, Alberto Delmás Lascorz, Mostafa Mahmoud, Miloš Nikolić, Kevin Siu, Dylan Malone Stuart +2 more
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
88
citations
afffundunlabeled
Proteus
Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor M. Aamodt, Natalie Enright Jerger, Andreas Moshovos
2016· article· en· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
81
citations
afffundunlabeled
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang, Nikos Komodakis, Simon Lacoste-Julien, Matthew B. Blaschko +1 more
2018· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
81
citations
afffundunlabeled
Automated Vehicle Detection and Classification
Azzedine Boukerche, Abdul Jabbar Siddiqui, Abdelhamid Mammeri
2017· review· en· ACM Computing Surveys· Computer Science
machine prediction:candidate · noneconsensus · none
80
citations
affunlabeled
Dense Voxel Fusion for 3D Object Detection
Anas Mahmoud, Jordan S. K. Hu, Steven L. Waslander
2023· article· en· 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)· Computer Science
machine prediction:candidate · noneconsensus · none
74
citations
affunlabeled
Loom
Sayeh Sharify, Alberto Delmás Lascorz, Kevin Siu, Patrick Judd, Andreas Moshovos
2018· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
72
citations
affunlabeled
Neural networks designing neural networks
Sean C. Smithson, Guang Yang, Warren J. Gross, Brett H. Meyer
2016· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
affunlabeled
You Cannot Improve What You Do not Measure
Andrew Boutros, Sadegh Yazdanshenas, Vaughn Betz
2018· article· en· ACM Transactions on Reconfigurable Technology and Systems· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations
affunlabeled
Annealing Knowledge Distillation
Aref Jafari, Mehdi Rezagholizadeh, Pranav Sharma, Ali Ghodsi
2021· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
69
citations

How this was built: Screen · Findings · About