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Found 5 papers, 1 papers with code
Dependence model assessment and selection with DecoupleNets
This map, termed DecoupleNet, is used for dependence model assessment and selection.
RafterNet: Probabilistic predictions in multi-response regression
A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced.
Applications of multivariate quasi-random sampling with neural networks
Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes.
Multivariate time-series modeling with generative neural networks
Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS).
Quasi-random sampling for multivariate distributions via generative neural networks
Once trained on pseudo-random samples from a parametric model or on real data, these neural networks only require a multivariate standard uniform randomized QMC point set as input and are thus fast in estimating expectations of interest under dependence with variance reduction.
