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There is an increasing interest in the application of deep learning architectures to tabular data.
Tabnet: Attentive interpretable tabular learning. arxiv 2019
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Simon Haykin · 1994
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The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P Bradley · 1997
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Matplotlib: A 2d graphics environment
J. D. Hunter · 2007
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Data Structures for Statistical Computing in Python
Wes McKinney · 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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Deep neural networks for anatomical brain segmentation
Alexander de Brebisson and Giovanni Montana · 2015
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Empirical evaluation of rectified activations in convolutional network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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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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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Deepenfm: Deep neural networks with encoder enhanced factorization machine
Qiang Sun, Zhinan Cheng, Yanwei Fu, Wenxuan Wang, Yu-Gang Jiang, and Xiangyang Xue · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke et al · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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The evolved transformer
Interpretable click-through rate prediction through hierarchical attention
Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, and Wei Wang · 2020
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Cuda, release: 10.2.89, 2020
NVIDIA, Péter Vingelmann, and Frank H.P. Fitzek · 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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Simple modifications to improve tabular neural networks
James Fiedler · 2021
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Transformers predicting the future. applying attention in next-frame and time series forecasting
Radostin Cholakov and Todor Kolev · 2021
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David So, Quoc Le, and Chen Liang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Dnf-net: A neural architecture for tabular data
Ami Abutbul, Gal Elidan, Liran Katzir, and Ran El-Yaniv · 2020
Cited alongside, same era.
Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan W. Cvitkovic, and Zohar S. Karnin · 2020
Cited alongside, same era.
Hanxiao Liu, Zihang Dai, David R. So, and Quoc V. Le · 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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seaborn: statistical data visualization
Michael L. Waskom · 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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