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Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used.
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Multi-task bayesian optimization
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Submodularity for data selection in machine translation
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K. Swersky, J. Snoek, and R. P. Adams · 2014
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Submodular subset selection for large-scale speech training data
K. Wei, Y. Liu, K. Kirchhoff, C. Bartels, and J. Bilmes · 2014
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
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Scalable bayesian optimization using deep neural networks
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
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A system for massively parallel hyperparameter tuning
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Coresets for data-efficient training of machine learning models, 2020
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Hyper-parameter optimization: A review of algorithms and applications
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CORDS: COResets and Data Subset selection for Efficient Learning, March 2021
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RETRIEVE: Coreset selection for efficient and robust semi-supervised learning
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