4 citations · 4 across the 2 of their papers we have counts for
3 papers
Breadth-first, Depth-next Training of Random Forests
Andreea Anghel, Nikolas Ioannou, Thomas Parnell +3
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures. An exact, state-of-the-art binary decision tree b…
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning
Dimitrios Sarigiannis, Thomas Parnell, Haris Pozidis
The combined algorithm selection and hyperparameter tuning (CASH) problem is characterized by large hierarchical hyperparameter spaces. Model-free hyperparameter tuning methods can…
Large-Scale Stochastic Learning using GPUs
Thomas Parnell, Celestine Dünner, Kubilay Atasu +2
In this work we propose an accelerated stochastic learning system for very large-scale applications. Acceleration is achieved by mapping the training algorithm onto massively paral…