Alex Ratner
Title
Cited by
Cited by
Year
Data programming: Creating large training sets, quickly
AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré
Advances in neural information processing systems, 3567-3575, 2016
2242016
Snorkel: Rapid training data creation with weak supervision
A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré
The VLDB Journal, 1-22, 2019
1762019
Learning to compose domain-specific transformations for data augmentation
AJ Ratner, H Ehrenberg, Z Hussain, J Dunnmon, C Ré
Advances in neural information processing systems, 3236-3246, 2017
912017
Learning the structure of generative models without labeled data
SH Bach, B He, A Ratner, C Ré
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
452017
Deepdive: Declarative knowledge base construction
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
ACM SIGMOD Record 45 (1), 60-67, 2016
432016
DeepDive: Declarative knowledge base construction
C Zhang, C Ré, M Cafarella, C De Sa, A Ratner, J Shin, F Wang, S Wu
Communications of the ACM 60 (5), 93-102, 2017
322017
Snorkel: Fast training set generation for information extraction
AJ Ratner, SH Bach, HR Ehrenberg, C Ré
Proceedings of the 2017 ACM international conference on management of data …, 2017
302017
Training complex models with multi-task weak supervision
A Ratner, B Hancock, J Dunnmon, F Sala, S Pandey, C Ré
Proceedings of the AAAI Conference on Artificial Intelligence 33, 4763-4771, 2019
222019
Snorkel drybell: A case study in deploying weak supervision at industrial scale
SH Bach, D Rodriguez, Y Liu, C Luo, H Shao, C Xia, S Sen, A Ratner, ...
Proceedings of the 2019 International Conference on Management of Data, 362-375, 2019
202019
Swellshark: A generative model for biomedical named entity recognition without labeled data
J Fries, S Wu, A Ratner, C Ré
arXiv preprint arXiv:1704.06360, 2017
172017
Data programming with ddlite: Putting humans in a different part of the loop
HR Ehrenberg, J Shin, AJ Ratner, JA Fries, C Ré
Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 1-6, 2016
162016
A kernel theory of modern data augmentation
T Dao, A Gu, AJ Ratner, V Smith, C De Sa, C Ré
Proceedings of machine learning research 97, 1528, 2019
152019
Incremental knowledge base construction using DeepDive
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
The VLDB Journal 26 (1), 81-105, 2017
142017
Snorkel metal: Weak supervision for multi-task learning
A Ratner, B Hancock, J Dunnmon, R Goldman, C Ré
Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018
132018
Weak supervision: the new programming paradigm for machine learning. Hazy Research
A Ratner, S Bach, P Varma, C Ré
12
Learning dependency structures for weak supervision models
P Varma, F Sala, A He, A Ratner, C Ré
arXiv preprint arXiv:1903.05844, 2019
102019
The Role of Massively Multi-Task and Weak Supervision in Software 2.0.
AJ Ratner, B Hancock, C Ré
CIDR, 2019
92019
Cross-Modal data programming enables rapid medical machine learning
J Dunnmon, A Ratner, N Khandwala, K Saab, M Markert, H Sagreiya, ...
arXiv preprint arXiv:1903.11101, 2019
72019
Sysml: The new frontier of machine learning systems
A Ratner, D Alistarh, G Alonso, P Bailis, S Bird, N Carlini, B Catanzaro, ...
arXiv preprint arXiv:1904.03257, 2019
62019
AMELIE accelerates Mendelian patient diagnosis directly from the primary literature
J Birgmeier, M Haeussler, CA Deisseroth, KA Jagadeesh, AJ Ratner, ...
bioRxiv, 171322, 2017
62017
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