Fetching the paper…
Reading the bibliography…
Tabular data synthesis is a long-standing research topic in machine learning.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
M.F. Hutchinson · 1989
Earlier work this paper cites.
Nonlinear Programming Theory and Algorithms
M.S. Bazaraa, H.D. Sherali, and C.M. Shetty · 1993
Earlier work this paper cites.
Contraceptive Method Choice
Tjen-Sien Lim · 1997
Earlier work this paper cites.
Spambase
Hopkins, Mark, Reeber, Erik, Forman, George & Suermondt, and Jaap · 1999
Earlier work this paper cites.
Smote: synthetic minority over-sampling technique
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer · 2002
Earlier work this paper cites.
Borderline-smote: A new over-sampling method in imbalanced data sets learning
Hui Han, Wen-Yuan Wang, and Bing-Huan Mao · 2005
Earlier work this paper cites.
Using cart to generate partially synthetic, public use microdata
P. Jerome Reiter · 2005
Earlier work this paper cites.
MAGIC Gamma Telescope
R. Bock · 2007
Earlier work this paper cites.
Adasyn: Adaptive synthetic sampling approach for imbalanced learning
Haibo He, Yang Bai, Edwardo Garcia, and Shutao Li · 2008
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
Wall-Following Robot Navigation Data
Freire, Ananda, Veloso, Marcus, Barreto, and Guilherme · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Shifting weights: Adapting object detectors from image to video
Kevin Tang, Vignesh Ramanathan, Li Fei-Fei, and Daphne Koller · 2012
Earlier work this paper cites.
UCI machine learning repository, 2013
M. Lichman · 2013
Earlier work this paper cites.
Self-paced learning with diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, and Alexander Hauptmann · 2014
Cited alongside, same era.
A proactive intelligent decision support system for predicting the popularity of online news
Kelwin Fernandes, Pedro Vinagre, and Paulo Cortez · 2015
Cited alongside, same era.
Integrating openstreetmap crowdsourced data and landsat time-series imagery for rapid land use/land cover (lulc) mapping: Case study of the laguna de bay area of the philippines
Brian A. Johnson and Kotaro Iizuka · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Phishing Websites
Lee Mohammad, Rami & McCluskey · 2015
Cited alongside, same era.
The synthetic data vault
Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 2018
Later among the works it cites.
Correlated discrete data generation using adversarial training
Shreyas Patel, Ashutosh Kakadiya, Maitrey Mehta, Raj Derasari, Rahul Patel, and Ratnik Gandhi · 2018
Later among the works it cites.
Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and V. D. Mihaela Schaar · 2019
Later among the works it cites.
Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from colombia, peru and mexico
Fabio Mendoza Palechor and Alexis de la Hoz Manotas · 2019
Later among the works it cites.
Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and lstm recurrent neural networks
C. Okan Sakar, S. Polat, Mete Katircioglu, and Yomi Kastro · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
Cited alongside, same era.
Boosting deep learning risk prediction with generative adversarial networks for electronic health records
Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, and Yan Liu · 2017
Cited alongside, same era.
Generating multi-label discrete electronic health records using generative adversarial networks
Edward Choi, Siddharth Biswal, A. Bradley Maline, Jon Duke, F. Walter Stewart, and Jimeng Sun · 2017
Cited alongside, same era.
Real-valued (medical) time series generation with recurrent conditional gans, 2017
Cristóbal Esteban, L. Stephanie Hyland, and Gunnar Rätsch · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
Cited alongside, same era.
Generating synthetic but plausible healthcare record datasets, 2018
Laura Aviñó, Matteo Ruffini, and Ricard Gavaldà · 2018
Cited alongside, same era.
Later among the works it cites.
Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
Later among the works it cites.
How to train your neural ode: the world of jacobian and kinetic regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan, and Adam M. Oberman · 2020
Later among the works it cites.
Multiclass classification of dry beans using computer vision and machine learning techniques
Murat Koklu and Ilker Ali Ozkan · 2020
Later among the works it cites.
Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
Later among the works it cites.
Oct-gan: Neural ode-based conditional tabular gans
Jayoung Kim, Jinsung Jeon, Jaehoon Lee, Jihyeon Hyeong, and Noseong Park · 2021
Later among the works it cites.
Invertible tabular GANs: Killing two birds with one stone for tabular data synthesis
Jaehoon Lee, Jihyeon Hyeong, Jinsung Jeon, Noseong Park, and Jihoon Cho · 2021
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
Later among the works it cites.
Tackling the generative learning trilemma with denoising diffusion gans, 12 2021
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
Later among the works it cites.
Sos: Score-based oversampling for tabular data
Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, and Jihoon Cho · 2022
Closest in time.