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 Machine Learning
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.

127 results · 1 filter active ·
Results by year
20022021
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.
127 works in the cohort · of 4,299,418page 2 of 3

Labels cover 0 of 127 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 127 of 127 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
Tails of Lipschitz Triangular Flows
Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
19
citations
affno abstractunlabeled
Learning Latent Space Models with Angular Constraints
Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou +1 more
2017· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
17
citations
affunlabeled
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Pashootan Vaezipoor, Andrew C. Li, Rodrigo A Toro Icarte, Sheila A. McIlraith
2021· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
DCM bandits: learning to rank with multiple clicks
Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen
2016· article· en· International Conference on Machine Learning· Decision Sciences
machine prediction:candidate · noneconsensus · none
15
citations
affunlabeled
Bidirectional Helmholtz machines
Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio
2016· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
11
citations
affunlabeled
Undirected Graphical Models as Approximate Posteriors
Arash Vahdat, Evgeny Andriyash, William G. Macready
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Causal Modeling for Fairness In Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2020· article· en· International Conference on Machine Learning· Social Sciences
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Differentially private policy evaluation
Borja Balle, Maziar Gomrokchi, Doina Precup
2016· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
Small-GAN: Speeding up GAN Training using Core-Sets
Samrath Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
10
citations
affunlabeled
IMEXnet - A Forward Stable Deep Neural Network
Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto
2019· article· en· International Conference on Machine Learning· Physics and Astronomy
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
Towards Binary-Valued Gates for Robust LSTM Training
Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang +1 more
2018· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
A Generative Process for Contractive Auto-Encoders.
Salah Rifai, Yann Dauphin, Pascal Vincent, Yoshua Bengio
2012· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affno abstractunlabeled
Selective Dyna-style Planning Under Limited Model Capacity
Muhammad Zaigham Zaheer, Samuel Sokota, Erin J. Talvitie, Martha White
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Markpainting: Adversarial Machine Learning meets Inpainting
David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson
2021· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affno abstractunlabeled
Per-Decision Option Discounting
Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup
2019· article· en· International Conference on Machine Learning· Economics, Econometrics and Finance
machine prediction:candidate · noneconsensus · none
4
citations
affno abstractunlabeled
On the Optimality of Batch Policy Optimization Algorithms
Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li +2 more
2021· article· en· International Conference on Machine Learning· Engineering
machine prediction:candidate · noneconsensus · none
4
citations
affunlabeled
Energy-Based Processes for Exchangeable Data
Sherry Yang, Bo Dai, Hanjun Dai, Dale Schuurmans
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affunlabeled
Multivariate Submodular Optimization.
Richard Santiago, F. Bruce Shepherd
2019· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
A Novel Method to Solve Neural Knapsack Problems
Duanshun Li, Dong‐Eun Lee, Ali Seyedmazloom, Giridhar Kaushik, Kookjin Lee, Noseong Park
2021· article· en· International Conference on Machine Learning· Engineering
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Deterministic Independent Component Analysis
Ruitong Huang, András György, Csaba Szepesv ri
2015· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Online Bayesian Moment Matching based SAT Solver Heuristics
Haonan Duan, Saeed Nejati, George Trimponias, Pascal Poupart, Vijay Ganesh
2020· article· en· International Conference on Machine Learning· Computer Science
machine prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About