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Foundation models have revolutionized tasks in computer vision and natural language processing.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Vime: Variational information maximizing exploration
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel · 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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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2018
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Google dataset search by the numbers
Omar Benjelloun, Shiyu Chen, and Natasha Noy · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A customer churn prediction model based on XGBoost and MLP
Qi Tang, Guoen Xia, Xianquan Zhang, and Feng Long · 2020
Cited alongside, same era.
Tabnet: Attentive interpretable tabular learning
Sercan Ö Arik and Tomas Pfister · 2021
Cited alongside, same era.
OpenML benchmarking suites
Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael Gomes Mantovani, Jan van Rijn, and Joaquin Vanschoren · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
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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Transformers can do Bayesian inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, and Frank Hutter · 2022
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Forecastpfn: Synthetically-trained zero-shot forecasting
Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha Naidu, and Colin White · 2023
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Scaling tabpfn: Sketching and feature selection for tabular prior-data fitted networks
Benjamin Feuer, Chinmay Hegde, and Niv Cohen · 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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Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
Cited alongside, same era.
Tabpfgen–tabular data generation with tabpfn
Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, and Anthony Caterini · 2023
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When do neural nets outperform boosted trees on tabular data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Ganesh Ramakrishnan, Micah Goldblum, Colin White, et al · 2023
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Findiff: Diffusion models for financial tabular data generation
Timur Sattarov, Marco Schreyer, and Damian Borth · 2023
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Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang · 2023
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