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pro vyhledávání: '"Olmin, Amanda"'
Autor:
Olmin, Amanda, Lindsten, Fredrik
Epoch-wise double descent is the phenomenon where generalisation performance improves beyond the point of overfitting, resulting in a generalisation curve exhibiting two descents under the course of learning. Understanding the mechanisms driving this
Externí odkaz:
http://arxiv.org/abs/2407.09845
Noise-contrastive estimation (NCE) is a popular method for estimating unnormalised probabilistic models, such as energy-based models, which are effective for modelling complex data distributions. Unlike classical maximum likelihood (ML) estimation th
Externí odkaz:
http://arxiv.org/abs/2402.16688
Autor:
Olmin, Amanda
Machine learning models are employed in several aspects of society, ranging from autonomous cars to justice systems. They affect your everyday life, for instance through recommendations on your streaming service and by informing decisions in healthca
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-183965
Publikováno v:
In: ICONIP. Communications in Computer and Information Science, vol 1792. Springer, Singapore (2023)
Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate those observations that are likely to give the most gain in model performance. We propose an active learn
Externí odkaz:
http://arxiv.org/abs/2204.08335
Autor:
Olmin, Amanda, Lindsten, Fredrik
Labelling of data for supervised learning can be costly and time-consuming and the risk of incorporating label noise in large data sets is imminent. When training a flexible discriminative model using a strictly proper loss, such noise will inevitabl
Externí odkaz:
http://arxiv.org/abs/2110.03321
Publikováno v:
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), Espoo, Finland, 2020, pp. 1-6
Ensembles of neural networks have been shown to give better performance than single networks, both in terms of predictions and uncertainty estimation. Additionally, ensembles allow the uncertainty to be decomposed into aleatoric (data) and epistemic
Externí odkaz:
http://arxiv.org/abs/2002.11531