Jonathan Bragg
Jonathan Bragg
Allen Institute for AI (AI2)
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Crowdsourcing Multi-Label Classification for Taxonomy Creation
J Bragg, Mausam, DS Weld
First AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2013), 2013
Effective crowd annotation for relation extraction
A Liu, S Soderland, J Bragg, CH Lin, X Ling, DS Weld
Proceedings of the 2016 Conference of the North American Chapter of the …, 2016
Microtalk: Using argumentation to improve crowdsourcing accuracy
R Drapeau, L Chilton, J Bragg, D Weld
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (1), 2016
Optimal Testing for Crowd Workers
J Bragg, Mausam, DS Weld
AAMAS, 2016
Parallel Task Routing for Crowdsourcing
J Bragg, A Kolobov, Mausam, DS Weld
Artificial Intelligence and Collective Intelligence
DS Weld, Mausam, CH Lin, J Bragg
The Collective Intelligence Handbook, 2014
Cicero: Multi-turn, contextual argumentation for accurate crowdsourcing
Q Chen, J Bragg, LB Chilton, DS Weld
Proceedings of the 2019 chi conference on human factors in computing systems …, 2019
Subcontracting microwork
MR Morris, JP Bigham, R Brewer, J Bragg, A Kulkarni, J Li, S Savage
Proceedings of the 2017 CHI conference on human factors in computing systems …, 2017
Genie: A leaderboard for human-in-the-loop evaluation of text generation
D Khashabi, G Stanovsky, J Bragg, N Lourie, J Kasai, Y Choi, NA Smith, ...
arXiv preprint arXiv:2101.06561, 2021
Sprout: Crowd-Powered Task Design for Crowdsourcing
J Bragg, Mausam, DS Weld
ACM Symposium on User Interface Software and Technology (UIST'18), 2018
Worker-owned cooperative models for training artificial intelligence
A Sriraman, J Bragg, A Kulkarni
Companion of the 2017 ACM Conference on Computer Supported Cooperative Work …, 2017
Flex: Unifying evaluation for few-shot nlp
J Bragg, A Cohan, K Lo, I Beltagy
arXiv preprint arXiv:2107.07170, 2021
Learning on the Job: Optimal Instruction for Crowdsourcing
J Bragg, Mausam, DS Weld
Neo-Riemannian cycle detection with weighted finite-state transducers
J Bragg, E Chew, S Shieber
12th International Society for Music Information Retrieval Conference (ISMIR …, 2011
Fake it till you make it: Learning-compatible performance support
J Bragg, E Brunskill
Uncertainty in Artificial Intelligence, 915-924, 2020
Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization
SW Huang, J Bragg, I Cowhey, O Etzioni, DS Weld
Proceedings of the 19th ACM Conference on Computer-Supported Cooperative …, 2016
Mathematics and Computation in Music 2009: John Clough Memorial Conference
J Bragg, CZA Huang
Computer Music Journal 34 (1), 100-102, 2010
Improving the Accessibility of Scientific Documents: Current State, User Needs, and a System Solution to Enhance Scientific PDF Accessibility for Blind and Low Vision Users
LL Wang, I Cachola, J Bragg, EYY Cheng, C Haupt, M Latzke, B Kuehl, ...
arXiv preprint arXiv:2105.00076, 2021
Self-Improving Crowdsourcing: Near-Effortless Design of Adaptive Distributed Work
J Bragg
PhD Thesis, 2018
Detection of Neo-Riemannian Cycles: A Finite State Approach
J Bragg
Harvard University, 2010
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