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With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios.
Knowledge acquisition and explanation for multi-attribute decision making
Bohanec, M. and Rajkovic, V · 1988
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An application for admission in public school systems
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A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
Lim, T.-S., Loh, W.-Y., and Shih, Y.-S · 2000
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Using cart to generate partially synthetic, public use microdata
Reiter, P. J · 2005
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Application of rule induction algorithms for analysis of data collected by seismic hazard monitoring systems in coal mines
Sikora, M. et al · 2010
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Application of a neuro fuzzy network in prediction of absenteeism at work
Martiniano, A., Ferreira, R., Sassi, R., and Affonso, C · 2012
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An assessment of features related to phishing websites using an automated technique
Mohammad, R. M., Thabtah, F., and McCluskey, L · 2012
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A cross–cultural grammar for temporal harmony in afro–latin musics: Clave, partido–alto and other timelines
Vurkaç, M · 2012
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A data-driven approach to predict the success of bank telemarketing
Moro, S., Cortez, P., and Rita, P · 2014
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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The synthetic data vault
Patki, N., Wedge, R., and Veeramachaneni, K · 2016
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Generating multi-label discrete electronic health records using generative adversarial networks
Choi, E., Biswal, S., Maline, A. B., Duke, J., Stewart, F. W., and Sun, J · 2017
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Real-valued (medical) time series generation with recurrent conditional gans, 2017
Esteban, C., Hyland, L. S., and Rätsch, G · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Privbayes: Private data release via bayesian networks
Zhang, J., Cormode, G., Procopiuc, C. M., Srivastava, D., and Xiao, X · 2017
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Generating synthetic but plausible healthcare record datasets, 2018
Aviñó, L., Ruffini, M., and Gavaldà, R · 2018
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Reliable fidelity and diversity metrics for generative models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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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
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
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Deep neural networks and tabular data: A survey
Borisov, V., Leemann, T., Seßler, K., Haug, J., Pawelczyk, M., and Kasneci, G · 2021
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
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Park, N., Mohammadi, M., Gorde, K., Jajodia, S., Park, H., and Kim, Y · 2018
Cited alongside, same era.
Pate-gan: Generating synthetic data with differential privacy guarantees
Jordon, J., Yoon, J., and Schaar, V. D. M · 2019
Cited alongside, same era.
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
Liu, T., Fan, W., and Wu, C · 2019
Cited alongside, same era.
Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from colombia, peru and mexico
Palechor, F. M. and de la Hoz Manotas, A · 2019
Cited alongside, same era.
Research center of sciences of communication, via sersale 117, 00128, rome, italy, 2019
provided by Semeion, D · 2019
Cited alongside, same era.
Modeling tabular data using conditional gan
Xu, L., Skoularidou, M., Cuesta-Infante, A., and Veeramachaneni, K · 2019
Cited alongside, same era.
How to train your neural ode: the world of jacobian and kinetic regularization
Finlay, C., Jacobsen, J.-H., Nurbekyan, L., and Oberman, A. M · 2020
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
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Oct-gan: Neural ode-based conditional tabular gans
Kim, J., Jeon, J., Lee, J., Hyeong, J., and Park, N · 2021
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Invertible tabular GANs: Killing two birds with one stone for tabular data synthesis
Lee, J., Hyeong, J., Jeon, J., Park, N., and Cho, J · 2021
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Health at a Glance 2021
OECD · 2021
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Ctab-gan: Effective table data synthesizing
Zhao, Z., Kunar, A., Birke, R., and Chen, L. Y · 2021
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An empirical study on the membership inference attack against tabular data synthesis models
Hyeong, J., Kim, J., Park, N., and Jajodia, S · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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