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While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data.
Large batch optimization for deep learning: Training bert in 76 minutes
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Lookahead optimizer: k steps forward, 1 step back
Zhang, M., Lucas, J., Ba, J., and Hinton, G. E · 1907
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On estimating regression
Nadaraya, E. A · 1964
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Smooth regression analysis
Watson, G. S · 1964
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On the nonparametric estimation of regression functions
Benedetti, J. K · 1977
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A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., and Vapnik, V. N · 1992
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Support-vector networks
Cortes, C. and Vapnik, V · 1995
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Random decision forests
Ho, T. K · 1995
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Random forests
Breiman, L · 2001
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Greedy function approximation: A gradient boosting machine
Friedman, J. H · 2001
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Learning with kernels - Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B. and Smola, A. J · 2002
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Metric learning for kernel regression
Weinberger, K. Q. and Tesauro, G · 2007
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NIST handbook of mathematical functions
Olver, F. W. J., Lozier, D. W., Boisvert, R. F., and Clark, C. W · 2010
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On the dual formulation of boosting algorithms
Shen, C. and Li, H · 2010
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Do we need hundreds of classifiers to solve real world classification problems?
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D · 2014
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Hyperopt-sklearn: automatic hyperparameter configuration for scikit-learn
Komer, B., Bergstra, J., and Eliasmith, C · 2014
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Deep learning in neural networks: An overview
Schmidhuber, J · 2014
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Are random forests truly the best classifiers?
Wainberg, M., Alipanahi, B., and Frey, B. J · 2016
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CatBoost: unbiased boosting with categorical features
Dorogush, A. V., Gulin, A., Gusev, G., Kazeev, N., Prokhorenkova, L. O., and Vorobev, A · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Ke, G., Meng, A., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
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Self-normalizing neural networks
Klambauer, G., Unterthiner, T., Mayr, A., and Hochreiter, S · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Bootstrap your own latent - a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. Á., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M · 2020
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TabTransformer: Tabular data modeling using contextual embeddings
Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z · 2020
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2020
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Modern Hopfield networks and attention for immune repertoire classification
Widrich, M., Schäfl, B., Pavlović, M., Ramsauer, H., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G. K., Greiff, V., Hochreiter, S., and Klambauer, G · 2020
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VIME: Extending the success of self- and semi-supervised learning to tabular domain
Yoon, J., Zhang, Y., Jordon, J., and vanDerSchaar, M · 2020
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Improving palliative care with deep learning
Avati, A., Jung, K., Harman, S., Downing, L., Ng, A., and Shah, N · 2018
Cited alongside, same era.
Large-scale comparison of machine learning methods for drug target prediction on chembl
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J., Ceulemans, H., Clevert, D., and Hochreiter, S · 2018
Cited alongside, same era.
CatBoost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A · 2018
Cited alongside, same era.
Regularization learning networks: Deep learning for tabular datasets
Shavitt, I. and Segal, E · 2018
Cited alongside, same era.
Crepurposing high-throughput image assays enables biological activity prediction for drug discovery
Simm, J., Klambauer, G., Arany, A., Steijaert, M., Wegner, J., Gustin, E., Chupakhin, V., Chong, Y., Vialard, J., Bujinsters, P., Velter, I., Vapirev, A., Singh, S., Carpenter, A., Wuyts, R., Hochreiter, S., Moreau, Y., and Ceulemans, H · 2018
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
TabNet: Attentive interpretable tabular learning
Arik, S. Ö. and Pfister, T · 2021
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Contrastive Mixup: self- and semi-supervised learning for tabular domain
Darabi, S., Fazeli, S., Pazoki, A., Sankararaman, S., and Sarrafzadeh, M · 2021
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TabularNet: A neural network architecture for understanding semantic structures of tabular data
Du, L., Gao, F., Chen, X., Jia, R., Wang, J., Zhang, J., Han, S., and Zhang, D · 2021
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Simple modifications to improve tabular neural networks
Fiedler, J · 2021
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Revisiting deep learning models for tabular data
Gorishniy, Y., Rubachev, I., Khrulkov, V., and Babenko, A · 2021
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TabGNN: Multiplex graph neural network for tabular data prediction
Guo, X., Quan, Y., Zhao, H., Yao, Q., Li, Y., and Tu, W · 2021
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Regularization is all you need: Simple neural nets can excel on tabular data
Kadra, A., Lindauer, M., Hutter, F., and Grabocka, J · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Kossen, J., Band, N., Lyle, C., Gomez, A. N., Rainforth, T., and Gal, Y · 2021
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Hopfield networks is all you need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., Greiff, V., Kreil, D., Kopp, M., Klambauer, G., Brandstetter, J., and Hochreiter, S · 2021
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Tabular Data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2021
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SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., and Goldstein, T · 2021
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When are deep networks really better than random forests at small sample sizes?
Xu, H., Ainsworth, M., Peng, Y.-C., Kusmanov, M., Panda, S., and Vogelstein, J. T · 2021
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