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Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Recursive deep models for semantic compositionality over a sentiment treebank
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Deep learning
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Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
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Hyperparameter search space pruning–a new component for sequential model-based hyperparameter optimization
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The movielens datasets: History and context
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Deep residual learning for image recognition
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The cityscapes dataset for semantic urban scene understanding
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On the state of the art of evaluation in neural language models
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Explaining explanations: An overview of interpretability of machine learning
Leilani H Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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Automated machine learning: methods, systems, challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Hyper-parameter optimization: A review of algorithms and applications
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Improving massively multilingual neural machine translation and zero-shot translation
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Generative agents: Interactive simulacra of human behavior
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Autonomous chemical research with large language models
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Large language models for automated data science: Introducing caafe for context-aware automated feature engineering
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Evoprompting: Language models for code-level neural architecture search
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Enhancing explainability of hyperparameter optimization via bayesian algorithm execution
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Why do machine learning practitioners still use manual tuning? a qualitative study
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Towards learning universal hyperparameter optimizers with transformers
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An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks
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Priorband: Practical hyperparameter optimization in the age of deep learning
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Automl in the age of large language models: Current challenges, future opportunities and risks
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Benchmarking large language models as ai research agents
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Large language models as optimizers
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Using large language models for hyperparameter optimization
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Kan: Kolmogorov-arnold networks
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