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Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information.
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
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Autocross: Automatic feature crossing for tabular data in real-world applications. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1936–1945
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Anomaly detection: A survey
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Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining . 785–794
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Lightgbm: A highly efficient gradient boosting decision tree
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Attention is all you need
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Table-to-text generation by structure-aware seq2seq learning. In Proceedings of the AAAI conference on artificial intelligence , Vol. 32
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Curvilinear distance metric learning
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Hyperimpute: Generalized iterative imputation with automatic model selection. In International Conference on Machine Learning . PMLR, 9916–9937
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PTab: Using the Pre-trained Language Model for Modeling Tabular Data
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Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020 · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin. 2020 · 2020
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ToTTo: A controlled table-to-text generation dataset
Ankur P Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 3505–3506
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Tableqa: a large-scale chinese text-to-sql dataset for table-aware sql generation
Ningyuan Sun, Xuefeng Yang, and Yunfeng Liu. 2020 · 2020
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Vime: Extending the success of self-and semi-supervised learning to tabular domain
Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar. 2020 · 2020
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Tabnet: Attentive interpretable tabular learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 6679–6687
Sercan Ö Arik and Tomas Pfister. 2021 · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko. 2021 · 2021
Cited alongside, same era.
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. 2022 · 2022
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Language Models are Realistic Tabular Data Generators
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasneci. 2023 · 2023
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TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter. 2023 · 2023
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GitTables: A Large-Scale Corpus of Relational Tables
Madelon Hulsebos, Ç agatay Demiralp, and Paul Groth. 2023 · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering. In The Eleventh International Conference on Learning Representations
Liyao Li, Haobo Wang, Liangyu Zha, Qingyi Huang, Sai Wu, Gang Chen, and Junbo Zhao. 2023 · 2023
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DIWIFT: Discovering Instance-wise Influential Features for Tabular Data. In Proceedings of the ACM Web Conference 2023 . 1673–1682
Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, and Xiuqiang He. 2023 · 2023
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Catch: Collaborative Feature Set Search for Automated Feature Engineering. In Proceedings of the ACM Web Conference 2023 . 1886–1896
Guoshan Lu, Haobo Wang, Saisai Yang, Jing Yuan, Guozheng Yang, Cheng Zang, Gang Chen, and Junbo Zhao. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Generative Table Pre-training Empowers Models for Tabular Prediction
Tianping Zhang, Shaowen Wang, Shuicheng Yan, Jian Li, and Qian Liu. 2023 · 2023
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Disentangled Causal Embedding With Contrastive Learning For Recommender System
Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, and Kun Gai. 2023 · 2023
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XTab: Cross-table Pretraining for Tabular Transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran. 2023 · 2023
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Energy-based Automated Model Evaluation. In The Twelfth International Conference on Learning Representations
Ru Peng, Heming Zou, Haobo Wang, Yawen Zeng, Zenan Huang, and Junbo Zhao. 2024 · 2024
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