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Found 5 papers, 3 papers with code
Cramér-Rao bound-informed training of neural networks for quantitative MRI
We find, however, that in heterogeneous parameter spaces, i. e. in spaces in which the variance of the estimated parameters varies considerably, good performance is hard to achieve and requires arduous tweaking of the loss function, hyper parameters, and the distribution of the training data in parameter space.
Reliable Time Prediction in the Markov Stochastic Block Model
We introduce the Markov Stochastic Block Model (MSBM): a growth model for community based networks where node attributes are assigned through a Markovian dynamic.
Markov Random Geometric Graph (MRGG): A Growth Model for Temporal Dynamic Networks
It is based on a Markovian latent space dynamic: consecutive latent points are sampled on the Euclidean Sphere using an unknown Markov kernel; and two nodes are connected with a probability depending on a unknown function of their latent geodesic distance.
Concentration inequality for U-statistics of order two for uniformly ergodic Markov chains
We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains.
Three rates of convergence or separation via U-statistics in a dependent framework
Despite the ubiquity of U-statistics in modern Probability and Statistics, their non-asymptotic analysis in a dependent framework may have been overlooked.
