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This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks.
Application of bayesian approach to numerical methods of global and stochastic optimization
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Graph-revised convolutional network
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2021
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Slaps: Self-supervision improves structure learning for graph neural networks
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Revisiting deep learning models for tabular data
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Well-tuned simple nets excel on tabular datasets
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
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Retrieval & interaction machine for tabular data prediction
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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
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Subtab: Subsetting features of tabular data for self-supervised representation learning
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Deep graph structure learning for robust representations: A survey
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A graph neural network framework for social recommendations
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Why do tree-based models still outperform deep learning on typical tabular data?
Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux · 2022
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Towards unsupervised deep graph structure learning
Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan · 2022
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Revisiting pretraining objectives for tabular deep learning
Ivan Rubachev, Artem Alekberov, Yury Gorishniy, and Artem Babenko · 2022
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Table2graph: Transforming tabular data to unified weighted graph
Kaixiong Zhou, Zirui Liu, Rui Chen, Li Li, and Xia Hu Soo-Hyun Choi · 2022
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T2g-former: Organizing tabular features into relation graphs promotes heterogeneous feature interaction
Jiahuan Yan, Jintai Chen, Yixuan Wu, Danny Z Chen, and Jian Wu · 2023
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