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Tabular data is the foundation of the information age and has been extensively studied.
Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Wei, J.; and Zou, K. 2019 · 1901
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Learning semantic annotations for tabular data
Chen, J.; Jiménez-Ruiz, E.; Horrocks, I.; and Sutton, C. 2019 · 1906
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Greedy function approximation: a gradient boosting machine
Friedman, J. H. 2001 · 2001
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TaBERT: Pretraining for joint understanding of textual and tabular data
Yin, P.; Neubig, G.; Yih, W.-t.; and Riedel, S. 2020 · 2005
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Tabtransformer: Tabular data modeling using contextual embeddings
Huang, X.; Khetan, A.; Cvitkovic, M.; and Karnin, Z. 2020 · 2012
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Xgboost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Learning data manipulation for augmentation and weighting
Hu, Z.; Tan, B.; Salakhutdinov, R. R.; Mitchell, T. M.; and Xing, E. P. 2019 · 2019
Earlier work this paper cites.
Modeling tabular data using conditional gan
Xu, L.; Skoularidou, M.; Cuesta-Infante, A.; and Veeramachaneni, K. 2019 · 2019
Cited alongside, same era.
Table2vec: Neural word and entity embeddings for table population and retrieval
Zhang, L.; Zhang, S.; and Balog, K. 2019 · 2019
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K.; Berthelot, D.; Carlini, N.; Zhang, Z.; Zhang, H.; Raffel, C. A.; Cubuk, E. D.; Kurakin, A.; and Li, C.-L. 2020 · 2020
Cited alongside, same era.
Vime: Extending the success of self-and semi-supervised learning to tabular domain
Yoon, J.; Zhang, Y.; Jordon, J.; and van der Schaar, M. 2020 · 2020
Cited alongside, same era.
Tabnet: Attentive interpretable tabular learning
Arik, S. Ö.; and Pfister, T. 2021 · 2021
Cited alongside, same era.
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Somepalli, G.; Goldblum, M.; Schwarzschild, A.; Bruss, C. B.; and Goldstein, T. 2021 · 2021
Later among the works it cites.
Subtab: Subsetting features of tabular data for self-supervised representation learning
Ucar, T.; Hajiramezanali, E.; and Edwards, L. 2021 · 2021
Later among the works it cites.
TUTA: tree-based transformers for generally structured table pre-training
Wang, Z.; Dong, H.; Jia, R.; Li, J.; Fu, Z.; Han, S.; and Zhang, D. 2021 · 2021
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Turl: Table understanding through representation learning
Deng, X.; Sun, H.; Lees, A.; Wu, Y.; and Yu, C. 2022 · 2022
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Why do tree-based models still outperform deep learning on tabular data?
Grinsztajn, L.; Oyallon, E.; and Varoquaux, G. 2022 · 2022
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Borisov, V.; Leemann, T.; Seßler, K.; Haug, J.; Pawelczyk, M.; and Kasneci, G. 2021 · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Gorishniy, Y.; Rubachev, I.; Khrulkov, V.; and Babenko, A. 2021 · 2021
Cited alongside, same era.
Tabbie: Pretrained representations of tabular data
Iida, H.; Thai, D.; Manjunatha, V.; and Iyyer, M. 2021 · 2021
Cited alongside, same era.
Unified quality assessment of in-the-wild videos with mixed datasets training
Li, D.; Jiang, T.; and Jiang, M. 2021 · 2021
Cited alongside, same era.
Li, J.; Li, D.; Xiong, C.; and Hoi, S. 2022 · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R.; and Armon, A. 2022 · 2022
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Xie, T.; Wu, C. H.; Shi, P.; Zhong, R.; Scholak, T.; Yasunaga, M.; Wu, C.-S.; Zhong, M.; Yin, P.; Wang, S. I.; et al. 2022 · 2022
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