26 citations · 27 across the 2 of their papers we have counts for
3 papers
Hybridised Loss Functions for Improved Neural Network Generalisation
Matthew C. Dickson, Anna S. Bosman, Katherine M. Malan
Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Spec…
A survey of benchmarking frameworks for reinforcement learning
Belinda Stapelberg, Katherine M. Malan
Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with ne…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…