MétaCan
Menu
Back to cohort
Record W4313478262 · doi:10.1109/msp.2022.3218478

Table of Contents

2023· article· en· W4313478262 on OpenAlexfundno aff
Bihan Wen, Saiprasad Ravishankar, Zhizhen Zhao, Raja Giryes, Jong Ye, Zhiyuan Zha, Xin Yuan, Jiantao Zhou, Ce Zhu, Jonathan Dong, Lorenzo Valzania, Antoine Maillard, Thanh-an Pham, Sylvain Gigan, Michaël Unser, Jian Zhang, Bin Chen, Ruiqin Xiong, Yongbing Zhang, Weisheng Dong, Jinjian Wu, Leida Li, Guangming Shi, Xin Li, Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg, Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knöll, Mehmet Akçakaya, Youzuo Lin, James Theiler, Dongdong Chen, Michael Davies, Matthias J. Ehrhardt, Carola‐Bibiane Schönlieb, Ferdia Sherry, Julián Tachella, Yoram Bresler, Subhadip Mukherjee, Andreas Hauptmann, Ozan Öktem, Marcelo Pereyra, Christian Jutten

Bibliographic record

VenueIEEE Signal Processing Magazine · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsnot available
FundersInstituto Superior TécnicoUniversità di BolognaUniversidade de LisboaInstitut Polytechnique de ParisKungliga Tekniska HögskolanQueen's UniversitySharif University of TechnologyMicrosoft ResearchUniversidade Estadual PaulistaNational Science FoundationBar-Ilan UniversityYonsei UniversityDurham UniversityUniversité de NantesQueen's University BelfastImperial College LondonDeutsches Zentrum für Luft- und RaumfahrtTélécom ParisMcGill UniversityUniversity of PittsburghEmory UniversityTsinghua UniversityChalmers Tekniska HögskolaMicrosoft
KeywordsComputer scienceTable (database)Data mining

Abstract

fetched live from OpenAlex

can be applied for computational imaging. We cover topics ranging from modelbased methods to more advanced physics-informed deep learning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.147
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.8530.789

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.273
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Signal Processing MagazineSame topicModel Reduction and Neural NetworksFrench-language works237,207