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Representation learning stands as one of the critical machine learning techniques across various domains.
Logistic regression
Raymond E Wright · 1995
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Random forests
Leo Breiman · 2001
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Early stopping-but when?
Lutz Prechelt · 2002
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Bayesian regression and classification
Christopher M Bishop, Michael E Tipping, et al · 2003
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
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Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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The group lasso for logistic regression
Lukas Meier, Sara Van De Geer, and Peter Bühlmann · 2008
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L2 regularization for learning kernels
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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A data-driven approach to predict the success of bank telemarketing
Sérgio Moro, Paulo Cortez, and Paulo Rita · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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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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Semi-supervised zero-shot classification with label representation learning
Xin Li, Yuhong Guo, and Dale Schuurmans · 2015
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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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Classification and regression trees
Leo Breiman · 2017
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Personalized fall risk assessment for long-term care services improvement
Suiyao Chen, William D Kearns, James L Fozard, and Mingyang Li · 2017
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Multi-state reliability demonstration tests
Suiyao Chen, Lu Lu, and Mingyang Li · 2017
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Generalized linear models
Trevor J Hastie and Daryl Pregibon · 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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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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A comparative study of categorical variable encoding techniques for neural network classifiers
Kedar Potdar, Taher S Pardawala, and Chinmay D Pai · 2017
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Sensitivity and uncertainty analysis of trace physical model parameters based on psbt benchmark using gaussian process emulator
Chen Wang, Xu Wu, and Tomasz Kozlowski · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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A data heterogeneity modeling and quantification approach for field pre-assessment of chloride-induced corrosion in aging infrastructures
Suiyao Chen, Lu Lu, Yisha Xiang, Qing Lu, and Mingyang Li · 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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Tabnn: A universal neural network solution for tabular data
Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, and Tie-Yan Liu · 2018
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Machine learning in agriculture: A review
Konstantinos G Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson, and Dionysis Bochtis · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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Secure and robust machine learning for healthcare: A survey
Adnan Qayyum, Junaid Qadir, Muhammad Bilal, and Ala Al-Fuqaha · 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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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
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Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
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Scarf: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 2021
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Surrogate-based bayesian calibration of thermal-hydraulics models based on psbt time-dependent benchmark data
Chen Wang, Xu Wu, and Tomasz Kozlowski · 2018
Cited alongside, same era.
Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity
Suiyao Chen, Nan Kong, Xuxue Sun, Hongdao Meng, and Mingyang Li · 2019
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Deep unsupervised feature selection
Ian Covert, Uygar Sumbul, and Su-In Lee · 2019
Cited alongside, same era.
Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua · 2019
Cited alongside, same era.
Analysis of the automl challenge series 2015-2018
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michéle Sebag, Alexander Statnikov, WeiWei Tu, and Evelyne Viegas · 2019
Cited alongside, same era.
Telco customer churn (11.1.3+), 2019
IBM · 2019
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 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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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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Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi · 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 · 2022
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Self-supervised representation learning: Introduction, advances, and challenges
Linus Ericsson, Henry Gouk, Chen Change Loy, and Timothy M Hospedales · 2022
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Yanghao Li, Kaiming He, et al · 2022
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Local contrastive feature learning for tabular data
Zhabiz Gharibshah and Xingquan Zhu · 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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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Masked autoencoders in 3d point cloud representation learning
Jincen Jiang, Xuequan Lu, Lizhi Zhao, Richard Dazeley, and Meili Wang · 2022
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Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning
Colorado J Reed, Ritwik Gupta, Shufan Li, Sarah Brockman, Christopher Funk, Brian Clipp, Salvatore Candido, Matt Uyttendaele, and Trevor Darrell · 2022
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Optimizing crop management with reinforcement learning and imitation learning
Ran Tao, Pan Zhao, Jing Wu, Nicolas F Martin, Matthew T Harrison, Carla Ferreira, Zahra Kalantari, and Naira Hovakimyan · 2022
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Shuting Tao, Peng Peng, and Hongwei Wang · 2022
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Optimizing nitrogen management with deep reinforcement learning and crop simulations
Jing Wu, Ran Tao, Pan Zhao, Nicolas F Martin, and Naira Hovakimyan · 2022
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Deep learning for precise robot position prediction in logistics
Chang Che, Bo Liu, Shulin Li, Jiaxin Huang, and Hao Hu · 2023
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Optimal test design for reliability demonstration under multi-stage acceptance uncertainties
Bingjie Wang, Lu Lu, Suiyao Chen, and Mingyang Li · 2023
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Chen Wang, Xu Wu, and Tomasz Kozlowski · 2023
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Hallucination improves the performance of unsupervised visual representation learning
Jing Wu, Jennifer Hobbs, and Naira Hovakimyan · 2023
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Jing Wu, Naira Hovakimyan, and Jennifer Hobbs · 2023
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Contrastive learning enhanced deep neural network with serial regularization for high-dimensional tabular data
Yao Wu, Donghua Zhu, and Xuefeng Wang · 2023
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