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Tabular data (or tables) are the most widely used data format in machine learning (ML).
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip Torr · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Table union search on open data
Fatemeh Nargesian, Erkang Zhu, Ken Q Pu, and Renée J Miller · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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C-store: a column-oriented dbms
Mike Stonebraker, Daniel J Abadi, Adam Batkin, Xuedong Chen, Mitch Cherniack, Miguel Ferreira, Edmond Lau, Amerson Lin, Sam Madden, Elizabeth O’Neil, et al · 2018
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CatBoost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, and Andrey Gulin · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Representation learning with contrastive predictive coding
Aaron Van den Oord, Yazhe Li, Oriol Vinyals, et al · 2018
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Josie: Overlap set similarity search for finding joinable tables in data lakes
Erkang Zhu, Dong Deng, Fatemeh Nargesian, and Renée J Miller · 2019
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AutoInt: Automatic feature interaction learning via self-attentive neural networks
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang · 2019
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Autocross: Automatic feature crossing for tabular data in real-world applications
Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang Yang · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
Cited alongside, same era.
Multimodal transformer for unaligned multimodal language sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2019
Cited alongside, same era.
Semantic enrichment for large-scale data analytics
Vincenzo Cutrona · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
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Efficient joinable table discovery in data lakes: A high-dimensional similarity-based approach
Yuyang Dong, Kunihiro Takeoka, Chuan Xiao, and Masafumi Oyamada · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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BEiT: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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Contrastive mixup: Self-and semi-supervised learning for tabular domain
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Jinsung Yoon, Yao Zhang, James Jordon, and Mihaela van der Schaar · 2020
Cited alongside, same era.
Customer transaction fraud detection using xgboost model
Yixuan Zhang, Jialiang Tong, Ziyi Wang, and Fengqiang Gao · 2020
Cited alongside, same era.
Data-driven risk assessment on urban pipeline network based on a cluster model
Zifeng Wang and Suzhen Li · 2020
Cited alongside, same era.
TabTransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Birds have four legs?! NumerSense: Probing numerical commonsense knowledge of pre-trained language models
Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren · 2020
Cited alongside, same era.
Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
Cited alongside, same era.
Sajad Darabi, Shayan Fazeli, Ali Pazoki, Sriram Sankararaman, and Majid Sarrafzadeh · 2021
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Tabnet: Attentive interpretable tabular learning
Sercan O Arık and Tomas Pfister · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Danets: Deep abstract networks for tabular data classification and regression
Jintai Chen, Kuanlun Liao, Yao Wan, Danny Z Chen, and Jian Wu · 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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TabGNN: Multiplex graph neural network for tabular data prediction
Xiawei Guo, Yuhan Quan, Huan Zhao, Quanming Yao, Yong Li, and Weiwei Tu · 2021
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Retrieval & interaction machine for tabular data prediction
Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, and Yong Yu · 2021
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Well-tuned simple nets excel on tabular datasets
Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka · 2021
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
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VATT: Transformers for multimodal self-supervised learning from raw video, audio and text
Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong · 2021
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Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity
William Fedus, Barret Zoph, and Noam Shazeer · 2021
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TABBIE: Pretrained representations of tabular data
Hiroshi Iida, Dung Thai, Varun Manjunatha, and Mohit Iyyer · 2021
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TURL: Table understanding through representation learning
Xiang Deng, Huan Sun, Alyssa Lees, You Wu, and Cong Yu · 2021
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Retrieving complex tables with multi-granular graph representation learning
Fei Wang, Kexuan Sun, Muhao Chen, Jay Pujara, and Pedro A Szekely · 2021
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The GatedTabTransformer. an enhanced deep learning architecture for tabular modeling
Radostin Cholakov and Todor Kolev · 2022
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SCARF: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 2022
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
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TableFormer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul · 2022
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Robust (controlled) table-to-text generation with structure-aware equivariance learning
Fei Wang, Zhewei Xu, Pedro Szekely, and Muhao Chen · 2022
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