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Fine-tuning a pre-trained deep neural network has become a successful paradigm in various machine learning tasks.
Predicting clicks: estimating the click-through rate for new ads
Matthew Richardson, Ewa Dominowska, and Robert Ragno · 2007
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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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Heterogeneous ensemble for feature drifts in data streams
Hai-Long Nguyen, Yew-Kwong Woon, Wee-Keong Ng, and Li Wan · 2012
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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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Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Auxiliary information regularized machine for multiple modality feature learning
Yang Yang, Han-Jia Ye, De-Chuan Zhan, and Yuan Jiang · 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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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
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Fine-tuning deep convolutional neural networks for distinguishing illustrations from photographs
Gota Gando, Taiga Yamada, Haruhiko Sato, Satoshi Oyama, and Masahito Kurihara · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Deep learning over multi-field categorical data - - A case study on user response prediction
Weinan Zhang, Tianming Du, and Jun Wang · 2016
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Credit card fraud detection using machine learning techniques: A comparative analysis
John O Awoyemi, Adebayo O Adetunmbi, and Samuel A Oluwadare · 2017
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Deepfm: A factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He · 2017
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In defense of the triplet loss for person re-identification
Alexander Hermans, Lucas Beyer, and Bastian Leibe · 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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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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Deep learning for fixed model reuse
Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang, and Zhi-Hua Zhou · 2017
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Credit risk analysis using machine and deep learning models
Peter Martey Addo, Dominique Guegan, and Bertrand Hassani · 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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One-pass learning with incremental and decremental features
Chenping Hou and Zhi-Hua Zhou · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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A survey on deep transfer learning
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu · 2018
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Deep learning for computer vision: A brief review
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, et al · 2018
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Optuna: A next-generation hyperparameter optimization framework
Learnable embedding sizes for recommender systems
Siyi Liu, Chen Gao, Yihong Chen, Depeng Jin, and Yong Li · 2021
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Heterogeneous few-shot model rectification with semantic mapping
Han-Jia Ye, De-Chuan Zhan, Yuan Jiang, and Zhi-Hua Zhou · 2021
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Deep learning for click-through rate estimation
Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, and Xiuqiang He · 2021
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Autoemb: Automated embedding dimensionality search in streaming recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Xiwang Yang · 2021
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You say factorization machine, I say neural network - it’s all in the activation
Chen Almagor and Yedid Hoshen · 2022
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Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Online learning from data streams with varying feature spaces
Ege Beyazit, Jeevithan Alagurajah, and Xindong Wu · 2019
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A comparative study of fine-tuning deep learning models for plant disease identification
Edna Chebet Too, Li Yujian, Sam Njuki, and Liu Yingchun · 2019
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Learning multiple local metrics: Global consideration helps
Han-Jia Ye, De-Chuan Zhan, Nan Li, and Yuan Jiang · 2019
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A machine learning approach for prediction of pregnancy outcome following IVF treatment
Md. Rafiul Hassan, Sadiq Al-Insaif, Muhammad Imtiaz Hossain, and Joarder Kamruzzaman · 2020
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Tabtransformer: Tabular data modeling using contextual embeddings
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 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.
Scarf: Self-supervised contrastive learning using random feature corruption
Dara Bahri, Heinrich Jiang, Yi Tay, and Donald Metzler · 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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LIFT: language-interfaced fine-tuning for non-language machine learning tasks
Tuan Dinh, Yuchen Zeng, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy yong Sohn, Dimitris S. Papailiopoulos, and Kangwook Lee · 2022
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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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Prediction with unpredictable feature evolution
Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou · 2022
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On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves
Malliga Subramanian, Kogilavani Shanmugavadivel, and PS Nandhini · 2022
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Transtab: Learning transferable tabular transformers across tables
Zifeng Wang and Jimeng Sun · 2022
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Tabllm: few-shot classification of tabular data with large language models
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 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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Transfer learning with deep tabular models
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, and Micah Goldblum · 2023
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Tabret: Pre-training transformer-based tabular models for unseen columns
Soma Onishi, Kenta Oono, and Kohei Hayashi · 2023
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Cross-modal fine-tuning: Align then refine
Junhong Shen, Liam Li, Lucio M Dery, Corey Staten, Mikhail Khodak, Graham Neubig, and Ameet Talwalkar · 2023
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Xtab: Cross-table pretraining for tabular transformers
Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, and Mahsa Shoaran · 2023
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