5 citations · 6 across the 8 of their papers we have counts for
3 papers · 1 filter
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
Evolutionary Mapping of Neural Networks to Spatial Accelerators
Alessandro Pierro, Jonathan Timcheck, Jason Yik +3
Spatial accelerators, composed of arrays of compute-memory integrated units, offer an attractive platform for deploying inference workloads with low latency and low energy consumpt…
Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization
Daphne Theodorakopoulos, Marcel Wever, Marius Lindauer
Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, fairness, or energy consumption. In practice, this requires trading off multip…