4 citations · 6 across the 11 of their papers we have counts for
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Multi-objective Hyperparameter Optimization in the Age of Deep Learning
Soham Basu, Frank Hutter, Danny Stoll
While Deep Learning (DL) experts often have prior knowledge about which hyperparameter settings yield strong performance, only few Hyperparameter Optimization (HPO) algorithms can…
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel +14
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a b…
Frozen Layers: Memory-efficient Many-fidelity Hyperparameter Optimization
Timur Carstensen, Neeratyoy Mallik, Frank Hutter +1
As model sizes grow, finding efficient and cost-effective hyperparameter optimization (HPO) methods becomes increasingly crucial for deep learning pipelines. While multi-fidelity H…