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Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks.
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Gain: Missing data imputation using generative adversarial nets
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Housing prices prediction with a deep learning and random forest ensemble
B. Afonso, L. Melo, W. Oliveira, S. Sousa, and L. Berton · 2019
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Optuna: A next-generation hyperparameter optimization framework
T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama · 2019
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Med3d: Transfer learning for 3d medical image analysis
S. Chen, K. Ma, and Y. Zheng · 2019
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Novel transfer learning approach for medical imaging with limited labeled data
L. Alzubaidi, M. Al-Amidie, A. Al-Asadi, A. J. Humaidi, O. Al-Shamma, M. A. Fadhel, J. Zhang, J. Santamaría, and Y. Duan · 2021
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Tabnet: Attentive interpretable tabular learning
S. O. Arık and T. Pfister · 2021
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Scarf: Self-supervised contrastive learning using random feature corruption
D. Bahri, H. Jiang, Y. Tay, and D. Metzler · 2021
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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
A. Bardes, J. Ponce, and Y. LeCun · 2021
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Deep neural networks and tabular data: A survey
V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci · 2021
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
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Neural oblivious decision ensembles for deep learning on tabular data
S. Popov, S. Morozov, and A. Babenko · 2019
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Coatnet: Marrying convolution and attention for all data sizes
Z. Dai, H. Liu, Q. V. Le, and M. Tan · 2021
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Simple modifications to improve tabular neural networks
J. Fiedler · 2021
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Revisiting deep learning models for tabular data
Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko · 2021
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metamimic: analysis of hyperparameter transferability for tabular data using mimic-iv database, 2021
M. Grzyb, Z. Trafas, K. Woźnica, and P. Biecek · 2021
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Masked autoencoders are scalable vision learners
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick · 2021
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Deep learning on small tabular dataset: Using transfer learning and image classification
V. Jain, M. Goel, and K. Shah · 2021
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Transfer learning for tabular data
L. Joffe · 2021
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Mimic-iv, 2021
A. Johnson, L. Bulgarelli, T. Pollard, S. Horng, L. A. Celi, and R. Mark · 2021
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Regularization is all you need: Simple neural nets can excel on tabular data
A. Kadra, M. Lindauer, F. Hutter, and J. Grabocka · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
J. Kossen, N. Band, C. Lyle, A. N. Gomez, T. Rainforth, and Y. Gal · 2021
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Ttnet: Tabular transfer network for few-samples prediction
Z. Li, D. Ding, X. Liu, P. Zhang, Y. Wu, and L. Ma · 2021
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A comprehensive ehr timeseries pre-training benchmark
M. McDermott, B. Nestor, E. Kim, W. Zhang, A. Goldenberg, P. Szolovits, and M. Ghassemi · 2021
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Public covid-19 x-ray datasets and their impact on model bias–a systematic review of a significant problem
B. G. Santa Cruz, M. N. Bossa, J. Sölter, and A. D. Husch · 2021
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Hopular: Modern hopfield networks for tabular data
B. Schäfl, L. Gruber, A. Bitto-Nemling, and S. Hochreiter · 2021
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Tabular data: Deep learning is not all you need
R. Shwartz-Ziv and A. Armon · 2021
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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
G. Somepalli, M. Goldblum, A. Schwarzschild, C. B. Bruss, and T. Goldstein · 2021
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Deep learning of hiv field-based rapid tests
V. Turbé, C. Herbst, T. Mngomezulu, S. Meshkinfamfard, N. Dlamini, T. Mhlongo, T. Smit, V. Cherepanova, K. Shimada, J. Budd, et al · 2021
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Subtab: Subsetting features of tabular data for self-supervised representation learning
T. Ucar, E. Hajiramezanali, and L. Edwards · 2021
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Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
R. Wang, R. Shivanna, D. Cheng, S. Jain, D. Lin, L. Hong, and E. Chi · 2021
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Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, and G. Rätsch · 2021
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Converting tabular data into images for deep learning with convolutional neural networks
Y. Zhu, T. Brettin, F. Xia, A. Partin, M. Shukla, H. Yoo, Y. A. Evrard, J. H. Doroshow, and R. L. Stevens · 2021
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On embeddings for numerical features in tabular deep learning
Y. Gorishniy, I. Rubachev, and A. Babenko · 2022
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Revisiting pretraining objectives for tabular deep learning
I. Rubachev, A. Alekberov, Y. Gorishniy, and A. Babenko · 2022
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Transtab: Learning transferable tabular transformers across tables
Z. Wang and J. Sun · 2022
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K. Woźnica, M. Grzyb, Z. Trafas, and P. Biecek · 2022
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Locally sparse neural networks for tabular biomedical data
J. Yang, O. Lindenbaum, and Y. Kluger · 2022
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