Fetching the paper…
Reading the bibliography…
Dedicated neural network (NN) architectures have been designed to handle specific data types (such as CNN for images or RNN for text), which ranks them among state-of-the-art methods for dealing with these data.
Classification and regression trees
L Breiman, JH Friedman, R Olshen, and CJ Stone · 1984
Earlier work this paper cites.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
A general framework for parallel distributed processing
David E Rumelhart, Geoffrey E Hinton, James L McClelland, et al · 1986
Earlier work this paper cites.
Entropy nets: from decision trees to neural networks
Ishwar Krishnan Sethi · 1990
Earlier work this paper cites.
Fast training algorithms for multilayer neural nets
Richard P Brent · 1991
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
Earlier work this paper cites.
Sparse spatial autoregressions
R. Kelley Pace and Ronald Barry · 1997
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Is random model better? on its accuracy and efficiency
Wei Fan, Haixun Wang, Philip S Yu, and Sheng Ma · 2003
Earlier work this paper cites.
Neural Networks for Pattern Recognition
C.M. Bishop, P.N.C.C.M. Bishop, G. Hinton, and Oxford University Press · 2003
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Earlier work this paper cites.
Casting random forests as artificial neural networks (and profiting from it)
Johannes Welbl · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Searching for exotic particles in high-energy physics with deep learning
Pierre Baldi, Peter Sadowski, and Daniel Whiteson · 2014
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Relating cascaded random forests to deep convolutional neural networks for semantic segmentation
David L Richmond, Dagmar Kainmueller, Michael Y Yang, Eugene W Myers, and Carsten Rother · 2015
Cited alongside, same era.
Neural random forests
Gérard Biau, Erwan Scornet, and Johannes Welbl · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Later among the works it cites.
Deep forest
Z. Zhou and J. Feng · 2019
Later among the works it cites.
Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
Later among the works it cites.
Optimization for deep learning: An overview
Ruo-Yu Sun · 2020
Later among the works it cites.
Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
Recurrent neural network for text classification with multi-task learning
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang · 2016
Cited alongside, same era.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Cited alongside, same era.
Deep forest: Towards an alternative to deep neural networks
Zhi-Hua Zhou and Ji Feng · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
Tabnn: A universal neural network solution for tabular data
Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, and Tie-Yan Liu · 2018
Cited alongside, same era.
Later among the works it cites.
Revisiting deep learning models for tabular data, 2021
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
Later among the works it cites.
Deep neural networks and tabular data: A survey, 2021
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2021
Later among the works it cites.
Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
Later among the works it cites.
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, and Tom Goldstein · 2021
Later among the works it cites.
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, and Isabelle Guyon · 2021
Later among the works it cites.
Why do tree-based models still outperform deep learning on tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 2022
Closest in time.