MambaTab: A Plug-and-Play Model for Learning Tabular Data

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980

Original

2014

Earlier work this paper cites.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” International Conference on Machine Learning

2015

Earlier work this paper cites.

T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

2016

Earlier work this paper cites.

J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450

Original

2016

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition

2016

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems

2017

Earlier work this paper cites.

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, “Self-normalizing neural networks,” Advances in Neural Information Processing Systems

2017

Earlier work this paper cites.

R. Wang, B. Fu, G. Fu, and M. Wang, “Deep & cross network for ad click predictions,” Proceedings of the ADKDD’17

2017

Earlier work this paper cites.

Special issue on deep reinforcement learning

S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural Networks · 2018

Cited alongside, same era.

A. F. Agarap, “Deep learning using rectified linear units (relu),” arXiv preprint arXiv:1803.08375

Original

2018

Cited alongside, same era.

W. Song, C. Shi, Z. Xiao, Z. Duan, Y. Xu, M. Zhang, and J. Tang, “Autoint: Automatic feature interaction learning via self-attentive neural networks,” ACM International Conference on Information and Knowledge Management

2019

Cited alongside, same era.

Y. Zhang, J. Tong, Z. Wang, and F. Gao, “Customer transaction fraud detection using xgboost model,” International Conference on Computer Engineering and Application (ICCEA)

2020

Cited alongside, same era.

X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin, “Tabtransformer: Tabular data modeling using contextual embeddings,” arXiv preprint arXiv:2012.06678

S. Ö. Arik and T. Pfister, “Tabnet: Attentive interpretable tabular learning,” in Proceedings of the AAAI conference on artificial intelligence

2021

Later among the works it cites.

Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, “Revisiting deep learning models for tabular data,” Advances in Neural Information Processing Systems

2021

Later among the works it cites.

D. Bahri, H. Jiang, Y. Tay, and D. Metzler, “Scarf: Self-supervised contrastive learning using random feature corruption,” arXiv preprint arXiv:2106.15147

Original

2021

Later among the works it cites.

Z. Wang and J. Sun, “Transtab: Learning transferable tabular transformers across tables,” Advances in Neural Information Processing Systems

2022

Later among the works it cites.

D. Y. Fu, T. Dao, K. K. Saab, A. W. Thomas, A. Rudra, and C. Re, “Hungry hungry hippos: Towards language modeling with state space models,” in International Conference on Learning Representations