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Tabular datasets are ubiquitous in data science applications.
“Assessing beijing’s pm2. 5 pollution: severity, weather impact, apec and winter heating,”
Xuan Liang, Tao Zou, Bin Guo, Shuo Li, Haozhe Zhang, Shuyi Zhang, Hui Huang, and Song Xi Chen, · 2015
Earlier work this paper cites.
“Gans trained by a two time-scale update rule converge to a local nash equilibrium,”
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter, · 2017
Earlier work this paper cites.
“Bert: Pre-training of deep bidirectional transformers for language understanding,”
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, · 2018
Earlier work this paper cites.
“Language models are unsupervised multitask learners,”
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, · 2018
Earlier work this paper cites.
“Synthesizing tabular data using generative adversarial networks.(2018),”
Lei Xu and Kalyan Veeramachaneni, · 2018
Earlier work this paper cites.
“On accurate evaluation of gans for language generation,”
Stanislau Semeniuta, Aliaksei Severyn, and Sylvain Gelly, · 2018
Earlier work this paper cites.
“Tabnet: Attentive interpretable tabular learning,”
Sercan O Arik and Tomas Pfister, · 2019
Earlier work this paper cites.
“Modeling tabular data using conditional gan,”
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni, · 2019
Earlier work this paper cites.
“Huggingface’s transformers: State-of-the-art natural language processing,”
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush, · 2019
Cited alongside, same era.
“Hierarchical transformers for long document classification,”
Raghavendra Pappagari, Piotr Zelasko, Jesús Villalba, Yishay Carmiel, and Najim Dehak, · 2019
Cited alongside, same era.
“HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization,”
Xingxing Zhang, Furu Wei, and Ming Zhou, · 2019
Cited alongside, same era.
“Videobert: A joint model for video and language representation learning,”
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid, · 2019
Cited alongside, same era.
“Clinicalbert: Modeling clinical notes and predicting hospital readmission,”
“TaBERT: Pretraining for joint understanding of textual and tabular data,”
Pengcheng Yin, Graham Neubig, Wen tau Yih, and Sebastian Riedel, · 2020
Closest in time.
Synthesizing Tabular Data using Conditional GAN
Lei Xu, · 2020
Closest in time.
“Synthesis of tabular financial data using generative adversarial networks,” 2020
Anton Karlsson and Torbjörn Sjöberg, · 2020
Closest in time.
“Working with deep generative models and tabular data imputation,”
Ramiro Daniel Camino, Christian Hammerschmidt, et al., · 2020
Closest in time.
“Generating synthetic data in finance: opportunities, challenges and pitfalls,”
Samuel Assefa, Danial Dervovic, Mahmoud Mahfouz, Tucker Balch, Prashant Reddy, and Manuela Veloso, · 2020
Closest in time.
“Using generative adversarial networks to synthesize artificial financial datasets,” 2020
Dmitry Efimov, Di Xu, Luyang Kong, Alexey Nefedov, and Archana Anandakrishnan, · 2020
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Kexin Huang, Jaan Altosaar, and Rajesh Ranganath, · 2019
Cited alongside, same era.
“Synthesizing credit card transactions,”
Erik R Altman, · 2019
Cited alongside, same era.
“TaPas: Weakly supervised table parsing via pre-training,”
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos, · 2020
Cited alongside, same era.
Closest in time.
“Biobert: a pre-trained biomedical language representation model for biomedical text mining,”
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang, · 2020
Closest in time.