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A challenging open question in deep learning is how to handle tabular data.
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Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Learning to explain: An information-theoretic perspective on model interpretation
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Multi-layered gradient boosting decision trees
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Tabnn: A universal neural network solution for tabular data
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Catboost: unbiased boosting with categorical features
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