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The transferability of deep neural networks (DNNs) has made significant progress in image and language processing.
Supervised and unsupervised discretization of continuous features
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AutoInt: Automatic feature interaction learning via self-attentive neural networks
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Language models are few-shot learners
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A machine learning approach for prediction of pregnancy outcome following IVF treatment
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Neural oblivious decision ensembles for deep learning on tabular data
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Self-training with noisy student improves ImageNet classification
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Why do tree-based models still outperform deep learning on typical tabular data?
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SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
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TransTab: Learning transferable tabular Transformers across tables
Zifeng Wang and Jimeng Sun · 2022
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Language models are realistic tabular data generators
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Tabpfn: A transformer that solves small tabular classification problems in a second
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Machine learning in finance: A topic modeling approach
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Deep neural networks and tabular data: A survey
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Limitations of language models in arithmetic and symbolic induction
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T2G-Former: Organizing tabular features into relation graphs promotes heterogeneous feature interaction
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Tablegpt: Towards unifying tables, nature language and commands into one gpt
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Generative table pre-training empowers models for tabular prediction
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XTab: Cross-table pretraining for tabular Transformers
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Towards cross-table masked pretraining for web data mining
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