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We introduce a few-shot learning framework for error detection.
Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1
G. E. Hinton, J. L. McClelland, and D. E. Rumelhart · 1986
Earlier work this paper cites.
Pattern matching: The gestalt approach
J. W. Ratcliff and D. E. Metzener · 1988
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparison to regularized likelihood methods
J. Platt · 2000
Earlier work this paper cites.
Data cleaning: Problems and current approaches
E. Rahm and H.-H. Do · 2000
Earlier work this paper cites.
A neural probabilistic language model
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin · 2003
Earlier work this paper cites.
Special issue on learning from imbalanced data sets
N. V. Chawla, N. Japkowicz, and A. Kotcz · 2004
Earlier work this paper cites.
Minimal-change integrity maintenance using tuple deletions
J. Chomicki and J. Marcinkowski · 2005
Earlier work this paper cites.
Enhancing text categorization with semantic-enriched representation and training data augmentation
X. Lu, B. Zheng, A. Velivelli, and C. Zhai · 2006
Earlier work this paper cites.
Duplicate record detection: A survey
A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios · 2007
Earlier work this paper cites.
Active learning for class imbalance problem
S. Ertekin, J. Huang, and C. L. Giles · 2007
Earlier work this paper cites.
Euclidean embedding of co-occurrence data
A. Globerson, G. Chechik, F. Pereira, and N. Tishby · 2007
Earlier work this paper cites.
Semi-supervised learning tutorial
X. Zhu · 2007
Earlier work this paper cites.
Quantitative data cleaning for large databases
J. M. Hellerstein · 2008
Earlier work this paper cites.
On Approximating Optimum Repairs for Functional Dependency Violations
S. Kolahi and L. V. S. Lakshmanan · 2009
Earlier work this paper cites.
An Introduction to Duplicate Detection
F. Naumann and M. Herschel · 2010
Earlier work this paper cites.
Wrangler: Interactive visual specification of data transformation scripts
S. Kandel, A. Paepcke, J. Hellerstein, and J. Heer · 2011
Earlier work this paper cites.
Statistical distortion: Consequences of data cleaning
T. Dasu and J. M. Loh · 2012
Earlier work this paper cites.
Principles of Data Integration
A. Doan, A. Y. Halevy, and Z. G. Ives · 2012
Earlier work this paper cites.
Foundations of Data Quality Management
W. Fan and F. Geerts · 2012
Earlier work this paper cites.
Towards certain fixes with editing rules and master data
W. Fan, J. Li, S. Ma, N. Tang, and W. Yu · 2012
Earlier work this paper cites.
Active learning
B. Settles · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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Discovering denial constraints
X. Chu, I. F. Ilyas, and P. Papotti · 2013
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Holistic data cleaning: Putting violations into context
X. Chu, I. F. Ilyas, and P. Papotti · 2013
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Nadeef: a commodity data cleaning system
M. Dallachiesa, A. Ebaid, A. Eldawy, A. Elmagarmid, I. F. Ilyas, M. Ouzzani, and N. Tang · 2013
Cited alongside, same era.
Imbalanced Learning: Foundations, Algorithms, and Applications
H. He and Y. Ma · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, et al · 2013
Cited alongside, same era.
Detecting data errors: Where are we and what needs to be done?
Z. Abedjan, X. Chu, D. Deng, R. C. Fernandez, I. F. Ilyas, M. Ouzzani, P. Papotti, M. Stonebraker, and N. Tang · 2016
Later among the works it cites.
DataXFormer: A robust transformation discovery system
Z. Abedjan, J. Morcos, I. F. Ilyas, P. Papotti, M. Ouzzani, and M. Stonebraker · 2016
Later among the works it cites.
Wide & deep learning for recommender systems
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah · 2016
Later among the works it cites.
Deep Learning
I. J. Goodfellow, Y. Bengio, and A. Courville · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
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Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data
J. W. Osborne · 2013
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Data curation at scale: The Data Tamer system
M. Stonebraker, D. Bruckner, I. F. Ilyas, G. Beskales, M. Cherniack, S. Zdonik, A. Pagan, and S. Xu · 2013
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Scorpion: Explaining away outliers in aggregate queries
E. Wu and S. Madden · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Word embeddings through hellinger pca
R. Lebret and R. Collobert · 2014
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A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov · 2016
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Character-aware neural language models
Y. Kim, Y. Jernite, D. Sontag, and A. M. Rush · 2016
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Highway long short-term memory rnns for distant speech recognition
Y. Zhang, G. Chen, D. Yu, K. Yaco, S. Khudanpur, and J. Glass · 2016
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Enriching word vectors with subword information
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Improving music source separation based on dnns through data augmentation and network blending
S. U. M. P. F. G. M. E. T. K. N. T. Y. Mitsufuji · 2017
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The effectiveness of data augmentation in image classification using deep learning
L. Perez and J. Wang · 2017
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Cleaning data with forbidden itemsets
J. Rammelaere, F. Geerts, and B. Goethals · 2017
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Snorkel: Rapid training data creation with weak supervision
A. Ratner, S. H. Bach, H. Ehrenberg, J. Fries, S. Wu, and C. Ré · 2017
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Holoclean: Holistic data repairs with probabilistic inference
T. Rekatsinas, X. Chu, I. F. Ilyas, and C. Ré · 2017
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2017
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Autoaugment: Learning augmentation policies from data
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le · 2018
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Auto-detect: Data-driven error detection in tables
Z. Huang and Y. He · 2018
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Explaining repaired data with cfds
J. Rammelaere and F. Geerts · 2018
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Software 2.0 and snorkel: Beyond hand-labeled data
C. Ré · 2018
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A formal framework for probabilistic unclean databases
C. D. Sa, I. F. Ilyas, B. Kimelfeld, C. Ré, and T. Rekatsinas · 2019
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