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Tabular data is among the oldest and most ubiquitous forms of data.
Dynamic programming
Richard Bellman · 1966
Earlier work this paper cites.
Approximating discrete probability distributions with dependence trees
CKCN Chow and Cong Liu · 1968
Earlier work this paper cites.
Interpolated estimation of markov source parameters from sparse data
Frederick Jelinek · 1980
Earlier work this paper cites.
Algorithms for finding patterns in strings, handbook of theoretical computer science (vol. a): algorithms and complexity, 1991
Alfred V Aho · 1991
Earlier work this paper cites.
Random decision forests
Tin Kam Ho · 1995
Earlier work this paper cites.
Sparse spatial autoregressions
R Kelley Pace and Ronald Barry · 1997
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, and Pascal Vincent · 2000
Earlier work this paper cites.
API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Impact of hba1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records
Beata Strack, Jonathan P DeShazo, Chris Gennings, Juan L Olmo, Sebastian Ventura, Krzysztof J Cios, and John N Clore · 2014
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
Earlier work this paper cites.
The synthetic data vault
N. Patki, R. Wedge, and K. Veeramachaneni · 2016
Earlier work this paper cites.
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, and Jimeng Sun · 2017
Earlier work this paper cites.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Earlier work this paper cites.
Privacy-preserving distributed linear regression on high-dimensional data
Adrià Gascón, Phillipp Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahur, and David Evans · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
Earlier work this paper cites.
Adversarial generation of natural language
Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Christopher Pal, and Aaron Courville · 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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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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Airline Passenger Name Record Generation using Generative Adversarial Networks
Alejandro Mottini, Alix Lheritier, and Rodrigo Acuna-Agost · 2018
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Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 2018
Cited alongside, same era.
Synthetic minority oversampling of vital statistics data with generative adversarial networks
Aki Koivu, Mikko Sairanen, Antti Airola, and Tapio Pahikkala · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Missing value imputation: a review and analysis of the literature (2006–2017)
Wei-Chao Lin and Chih-Fong Tsai · 2020
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Vaem: a deep generative model for heterogeneous mixed type data
Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, and Cheng Zhang · 2020
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Learning model-agnostic counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
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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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Do NLP models know numbers? probing numeracy in embeddings
Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer · 2020
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Generating privacy-preserving synthetic tabular data using oblivious variational autoencoders
L Vivek Harsha Vardhan and Stanley Kok · 2020
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
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TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel · 2020
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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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Synthesising multi-modal minority samples for tabular data
Sajad Darabi and Yotam Elor · 2021
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Copula flows for synthetic data generation
Sanket Kamthe, Samuel Assefa, and Marc Deisenroth · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
Jannik Kossen, Neil Band, Clare Lyle, Aidan Gomez, Tom Rainforth, and Yarin Gal · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2021
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Tabular transformers for modeling multivariate time series
Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, and Erik Altman · 2021
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Lift: Language-interfaced fine-tuning for non-language machine learning tasks
Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Shashank Rajput, Michael Gira, Jy-yong Sohn, Dimitris Papailiopoulos, and Kangwook Lee · 2022
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Synthetic data generation for tabular health records: A systematic review
Mikel Hernandez, Gorka Epelde, Ane Alberdi, Rodrigo Cilla, and Debbie Rankin · 2022
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