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The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial.
Analysing mathematical reasoning abilities of neural models
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Support-vector networks
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Random forests
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Pattern recognition and machine learning , volume 4
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Collaborative hyperparameter tuning
Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag. 2013 · 2013
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Openml: networked science in machine learning
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Efficient and robust automated machine learning
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Learning to learn by gradient descent by gradient descent
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin. 2016 · 2016
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Two-stage transfer surrogate model for automatic hyperparameter optimization
Martin Wistuba, Nicolas Schilling, and Lars Schmidt-Thieme. 2016 · 2016
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017 · 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 · 2017
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A tutorial on bayesian optimization
Peter I Frazier. 2018 · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto. 2018 · 2018
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Automated machine learning: methods, systems, challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren. 2019 · 2019
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Auto-weka: Automatic model selection and hyperparameter optimization in weka
Lars Kotthoff, Chris Thornton, Holger H Hoos, Frank Hutter, and Kevin Leyton-Brown. 2019 · 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 · 2019
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Language Models are Few-Shot Learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2022 · 2022
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Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, E. Chi, Tatsunori Hashimoto, Oriol Vinyals, P. Liang, J. Dean, and W. Fedus. 2022 · 2022
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Privacy-preserving online automl for domain-specific face detection
Chenqian Yan, Yuge Zhang, Quanlu Zhang, Yaming Yang, Xinyang Jiang, Yuqing Yang, and Baoyuan Wang. 2022 · 2022
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Generate rather than retrieve: Large language models are strong context generators
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Hyperstar: Task-aware hyperparameters for deep networks
Gaurav Mittal, Chang Liu, Nikolaos Karianakis, Victor Fragoso, Mei Chen, and Yun Fu. 2020 · 2020
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Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml
Sebastian Pineda Arango, Hadi Samer Jomaa, Martin Wistuba, and Josif Grabocka. 2021 · 2021
Cited alongside, same era.
Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2021
Cited alongside, same era.
Representing numbers in nlp: a survey and a vision
Avijit Thawani, Jay Pujara, Pedro A Szekely, and Filip Ilievski. 2021 · 2021
Cited alongside, same era.
Towards learning universal hyperparameter optimizers with transformers
Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc’aurelio Ranzato, et al. 2022 · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, and others. 2022 · 2022
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Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2022 · 2022
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Deepke: A deep learning based knowledge extraction toolkit for knowledge base population
Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, et al. 2022 · 2022
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Emergent autonomous scientific research capabilities of large language models
Daniil A. Boiko, Robert MacKnight, and Gabe Gomes. 2023 · 2023
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Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023 · 2023
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OpenAI. 2023 · 2023
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Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023 · 2023
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Llama: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aur’elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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Can gpt-4 perform neural architecture search?
Mingkai Zheng, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu, and Samuel Albanie. 2023 · 2023
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