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This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach.
Minimization by random search techniques
Francisco J Solis and Roger J-B Wets · 1981
Earlier work this paper cites.
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
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, G.s Corrado, Kai Chen, and Jeffrey Dean · 2013
Earlier work this paper cites.
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
Earlier work this paper cites.
End-to-end training of differentiable pipelines across machine learning frameworks
Mitar Milutinovic, Atilim Gunes Baydi, Robert Zinkov, William Harvey, Dawn Song, and Frank Wood · 2017
Earlier work this paper cites.
ATM: A distributed, collaborative, scalable system for automated machine learning
Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, and Kalyan Veeramachaneni · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Autostacker: A compositional evolutionary learning system
Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, and Hod Lipson · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
AlphaD3M: Machine learning pipeline synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni Lourenco, Jorge One, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2018
Cited alongside, same era.
Probabilistic matrix factorization for automated machine learning
Nicolo Fusi, Rishit Sheth, and Melih Elibol · 2018
Cited alongside, same era.
Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
Cited alongside, same era.
BYU’s python library of useable tools for metalearning, 2019
Announcing automated ML capability in Azure Machine Learning, 2019
Deepak Mukunthu · 2019
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TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal S Olson and Jason H Moore · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2019
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Megatron-LM: Training multi-billion parameter language models using GPU model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
Later among the works it cites.
OBOE: Collaborative filtering for AutoML model selection
Chengrun Yang, Yuji Akimoto, Dae Won Kim, and Madeleine Udell · 2019
Later among the works it cites.
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BYU-DML · 2019
Cited alongside, same era.
AutoML: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2019
Cited alongside, same era.
Fast task-aware architecture inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, and Jesse Berent · 2019
Cited alongside, same era.
Automatic machine learning by pipeline synthesis using model-based reinforcement learning and a grammar
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni Lourenco, Kyunghyun Cho, Claudio Silva, and Juliana Freire
Cited in the paper.
Automl using metadata language embeddings
Iddo Drori, Lu Liu, Sharath Koorathota, Nian Yi, Jie Li, Antonio Khalil Moretti, Juliana Freire, and Madeleine Udell
Cited in the paper.
Marc-André Zöller and Marco F Huber · 2019
Later among the works it cites.
AutoML pipeline selection: Efficiently navigating the combinatorial space
Chengrun Yang, Ziyang Wu, Jicong Fan, and Madeleine Udell · 2020
Later among the works it cites.