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The widespread enthusiasm for deep learning has recently expanded into the domain of tabular data.
On the limited memory bfgs method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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Distance metric learning with application to clustering with side-information
Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell · 2002
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Neighbourhood components analysis
Jacob Goldberger, Sam T. Roweis, Geoffrey E. Hinton, and Ruslan Salakhutdinov · 2004
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Metric learning by collapsing classes
Amir Globerson and Sam T. Roweis · 2005
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Pattern recognition and machine learning
Christopher Bishop · 2006
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Statistical comparisons of classifiers over multiple data sets
Janez Demsar · 2006
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Information-theoretic metric learning
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S. Dhillon · 2007
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Predicting clicks: estimating the click-through rate for new ads
Matthew Richardson, Ewa Dominowska, and Robert Ragno · 2007
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Learning a nonlinear embedding by preserving class neighbourhood structure
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2007
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Distance metric learning for large margin nearest neighbor classification
Kilian Q. Weinberger and Lawrence K. Saul · 2009
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An empirical comparison of machine learning models for time series forecasting
Nesreen K Ahmed, Amir F Atiya, Neamat El Gayar, and Hisham El-Shishiny · 2010
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Deep supervised t-distributed embedding
Martin Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony J. Bonner, and Zhaolei Zhang · 2010
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Information retrieval perspective to nonlinear dimensionality reduction for data visualization
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski · 2010
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Metric learning: A survey
Brian Kulis · 2013
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Stochastic k-neighborhood selection for supervised and unsupervised learning
Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, and Richard S. Zemel · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández Delgado, Eva Cernadas, Senén Barro, and Dinani Gomes Amorim · 2014
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Floppies: a framework for large-scale ontology population of product information from tabular data in e-commerce stores
Lennart J Nederstigt, Steven S Aanen, Damir Vandic, and Flavius Frasincar · 2014
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Sparse compositional metric learning
Yuan Shi, Aurélien Bellet, and Fei Sha · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
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Deep metric learning for person re-identification
Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z. Li · 2014
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Metric Learning
Aurélien Bellet, Amaury Habrard, and Marc Sebban · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Similarity learning for high-dimensional sparse data
Kuan Liu, Aurélien Bellet, and Fei Sha · 2015
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Facenet: A unified embedding for face recognition and clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Lei Jimmy 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
Cited alongside, same era.
Wide & deep learning for recommender systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Deep metric learning via lifted structured feature embedding
DCN V2: improved deep & cross network and practical lessons for web-scale learning to rank systems
Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed H. Chi · 2021
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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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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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NODE-GAM: neural generalized additive model for interpretable deep learning
Chun-Hao Chang, Rich Caruana, and Anna Goldenberg · 2022
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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 · 2022
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Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Tim Lillicrap, Koray Kavukcuoglu, and Daan Wierstra · 2016
Cited alongside, same era.
Deepfm: A factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
Cited alongside, same era.
Collaborative metric learning
Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge J. Belongie, and Deborah Estrin · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Catboost: unbiased boosting with categorical features
Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
Cited alongside, same era.
On embeddings for numerical features in tabular deep learning
Yury Gorishniy, Ivan Rubachev, and Artem Babenko · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Léo Grinsztajn, Edouard Oyallon, and Gaël Varoquaux · 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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SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Avi Schwarzschild, Micah Goldblum, C. Bayan Bruss, and Tom Goldstein · 2022
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Tabcaps: A capsule neural network for tabular data classification with bow routing
Jintai Chen, KuanLun Liao, Yanwen Fang, Danny Chen, and Jian Wu · 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
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Tangos: Regularizing tabular neural networks through gradient orthogonalization and specialization
Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, and Mihaela van der Schaar · 2023
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When do neural nets outperform boosted trees on tabular data?
Duncan C. McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C., Ganesh Ramakrishnan, Micah Goldblum, and Colin White · 2023
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Collaborative residual metric learning
Tianjun Wei, Jianghong Ma, and Tommy W. S. Chow · 2023
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Unlocking the transferability of tokens in deep models for tabular data
Qi-Le Zhou, Han-Jia Ye, Leye Wang, and De-Chuan Zhan · 2023
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Can a deep learning model be a sure bet for tabular prediction?
Jintai Chen, Jiahuan Yan, Qiyuan Chen, Danny Ziyi Chen, Jian Wu, and Jimeng Sun · 2024
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Tabr: Tabular deep learning meets nearest neighbors in 2023
Yury Gorishniy, Ivan Rubachev, Nikolay Kartashev, Daniil Shlenskii, Akim Kotelnikov, and Artem Babenko · 2024
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Better by default: Strong pre-tuned mlps and boosted trees on tabular data
David Holzmüller, Léo Grinsztajn, and Ingo Steinwart · 2024
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Protogate: Prototype-based neural networks with global-to-local feature selection for tabular biomedical data
Xiangjian Jiang, Andrei Margeloiu, Nikola Simidjievski, and Mateja Jamnik · 2024
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Towards quantifying the effect of datasets for benchmarking: A look at tabular machine learning
Ravin Kohli, Matthias Feurer, Katharina Eggensperger, Bernd Bischl, and Frank Hutter · 2024
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TALENT: A tabular analytics and learning toolbox
Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, and Han-Jia Ye · 2024
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Retrieval & fine-tuning for in-context tabular models
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, and Anthony L. Caterini · 2024
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A data-centric perspective on evaluating machine learning models for tabular data
Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, and Heiner Stuckenschmidt · 2024
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Switchtab: Switched autoencoders are effective tabular learners
Jing Wu, Suiyao Chen, Qi Zhao, Renat Sergazinov, Chen Li, Shengjie Liu, Chongchao Zhao, Tianpei Xie, Hanqing Guo, Cheng Ji, Daniel Cociorva, and Hakan Brunzell · 2024
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Understanding the limits of deep tabular methods with temporal shift
Hao-Run Cai and Han-Jia Ye · 2025
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Tabpfn unleashed: A scalable and effective solution to tabular classification problems
Si-Yang Liu and Han-Jia Ye · 2025
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Tabred: A benchmark of tabular machine learning in-the-wild
Ivan Rubachev, Nikolay Kartashev, Yury Gorishniy, and Artem Babenko · 2025
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A closer look at tabpfn v2: Strength, limitation, and extension
Han-Jia Ye, Si-Yang Liu, and Wei-Lun Chao · 2025
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