3 papers
cs.LG2021
Scaling Federated Learning for Fine-tuning of Large Language Models
Agrin Hilmkil, Sebastian Callh, Matteo Barbieri +3
Federated learning (FL) is a promising approach to distributed compute, as well as distributed data, and provides a level of privacy and compliance to legal frameworks. This makes…
cs.SD2020
Perceiving Music Quality with GANs
Agrin Hilmkil, Carl Thomé, Anders Arpteg
Several methods have been developed to assess the perceptual quality of audio under transforms like lossy compression. However, they require paired reference signals of the unalter…
cs.LG2018
Towards Machine Learning on data from Professional Cyclists
Agrin Hilmkil, Oscar Ivarsson, Moa Johansson +2
Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and com…