Fetching the paper…
Reading the bibliography…
Tabular data are ubiquitous in real world applications.
Discovering rules by induction from large collections of examples
Quinlan, J. R. 1979 · 1979
Earlier work this paper cites.
Classification and Regression Trees
Breiman, L.; et al. 1984 · 1984
Earlier work this paper cites.
Random decision forests
Ho, T. K. 1995 · 1995
Earlier work this paper cites.
Greedy function approximation: A gradient boosting machine
Friedman, J. H. 2001 · 2001
Earlier work this paper cites.
Learning from attribute value taxonomies and partially specified instances
Zhang, J.; and Honavar, V. 2003 · 2003
Earlier work this paper cites.
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Zhang, J.; Kang, D.-K.; et al. 2006 · 2006
Earlier work this paper cites.
Graphical Models, Exponential Families, and Variational Inference
Wainwright, M. J.; and Jordan, M. I. 2008 · 2008
Earlier work this paper cites.
Rectified linear units improve restricted Boltzmann machines
Nair, V.; and Hinton, G. E. 2010 · 2010
Earlier work this paper cites.
The million song dataset
Bertin-Mahieux, T.; et al. 2011 · 2011
Earlier work this paper cites.
Web-search ranking with initialized gradient boosted regression trees
Mohan, A.; et al. 2011 · 2011
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y.; et al. 2013 · 2013
Earlier work this paper cites.
An Introduction to Statistical Learning
James, G.; et al. 2013 · 2013
Earlier work this paper cites.
Introducing LETOR 4.0 Datasets
Qin, T.; and Liu, T. 2013 · 2013
Earlier work this paper cites.
Practical lessons from predicting clicks on Ads at Fackbook
He, X.; et al. 2014 · 2014
Earlier work this paper cites.
C4.5: Programs for Machine Learning
Quinlan, J. R. 2014 · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N.; et al. 2014 · 2014
Cited alongside, same era.
XGBoost: A scalable tree boosting system
Chen, T.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; et al. 2016 · 2016
Cited alongside, same era.
Applying data mining techniques for increasing implantation rate by selecting best sperms for intra-cytoplasmic sperm injection treatment
Mirroshandel, S. A.; et al. 2016 · 2016
Cited alongside, same era.
Language modeling with gated convolutional networks
Dauphin, Y. N.; et al. 2017 · 2017
Cited alongside, same era.
DeepFM: A factorization-machine based neural network for CTR prediction
Guo, H.; Tang, R.; et al. 2017 · 2017
Cited alongside, same era.
TabNN: A universal neural network solution for tabular data
Ke, G.; et al. 2018 · 2018
Later among the works it cites.
Random hinge forest for differentiable learning
Lay, N.; et al. 2018 · 2018
Later among the works it cites.
CatBoost: Unbiased boosting with categorical features
Prokhorenkova, L.; et al. 2018 · 2018
Later among the works it cites.
Deep learning detecting fraud in credit card transactions
Roy, A.; et al. 2018 · 2018
Later among the works it cites.
Deep neural decision trees
Yang, Y.; et al. 2018 · 2018
Later among the works it cites.
E.T.-RNN: Applying deep learning to credit loan applications
Babaev, D.; et al. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Train longer, generalize better: Closing the generalization gap in large batch training of neural networks
Hoffer, E.; et al. 2017 · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G.; et al. 2017 · 2017
Cited alongside, same era.
LightGBM: A highly efficient gradient boosting decision tree
Ke, G.; et al. 2017 · 2017
Cited alongside, same era.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R.; et al. 2017 · 2017
Cited alongside, same era.
Deep Forest: Towards an alternative to deep neural networks
Zhou, Z.-H.; and Feng, J. 2017 · 2017
Cited alongside, same era.
Credit risk analysis using machine and deep learning models
Addo, P. M.; et al. 2018 · 2018
Cited alongside, same era.
Ke, G.; et al. 2019 · 2019
Later among the works it cites.
Quasi-hyperbolic momentum and Adam for deep learning
Ma, J.; and Yarats, D. 2019 · 2019
Later among the works it cites.
Sparse sequence-to-sequence models
Peters, B.; et al. 2019 · 2019
Later among the works it cites.
Neural oblivious decision ensembles for deep learning on tabular data
Popov, S.; et al. 2019 · 2019
Later among the works it cites.
TabNet: Attentive interpretable tabular learning
Arik, S. O.; and Pfister, T. 2020 · 2020
Later among the works it cites.
A machine learning approach for prediction of pregnancy outcome following IVF treatment
Hassan, M. R.; et al. 2020 · 2020
Later among the works it cites.
RepVGG: Making VGG-style ConvNets great again
Ding, X.; et al. 2021 · 2021
Closest in time.
DNF-Net: Effective deep modeling of tabular data
Katzir, L.; Elidan, G.; et al. 2021 · 2021
Closest in time.