5 papers
Multi-scale decomposition of sea surface height snapshots using machine learning
Jingwen Lyu, Yue Wang, Christian Pedersen +2
Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surfa…
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu +4
In Elmarakeby et al., "Biologically informed deep neural network for prostate cancer discovery", a feedforward neural network with biologically informed, sparse connections (P-NET)…
Learnable wavelet neural networks for cosmological inference
Christian Pedersen, Michael Eickenberg, Shirley Ho
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astro…
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Christian Pedersen, Laure Zanna, Joan Bruna +1
Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution s…
Towards Machine Learning-Based Meta-Studies: Applications to Cosmological Parameters
Tom Crossland, Pontus Stenetorp, Daisuke Kawata +8
We develop a new model for automatic extraction of reported measurement values from the astrophysical literature, utilising modern Natural Language Processing techniques. We use th…