Perturb-and-max-product: Sampling and learning in discrete energy-based models
M Lazaro-Gredilla, A Dedieu… - Advances in Neural …, 2021 - proceedings.neurips.cc
Perturb-and-MAP offers an elegant approach to approximately sample from a energy-based
model (EBM) by computing the maximum-a-posteriori (MAP) configuration of a perturbed …
model (EBM) by computing the maximum-a-posteriori (MAP) configuration of a perturbed …
Probabilistic personalised cascade with abstention
T Shpakova, N Sokolovska - Pattern Recognition Letters, 2021 - Elsevier
Cascade learning with abstention and individualised feature selection is a class of models in
high demand in personalised medical applications. The cascade consists of sequential …
high demand in personalised medical applications. The cascade consists of sequential …
On the parameter learning for Perturb-and-MAP models
T Shpakova - 2019 - theses.hal.science
Probabilistic graphical models encode hidden dependencies between random variables for
data modelling. Parameter estimation is a crucial part of handling such probabilistic models …
data modelling. Parameter estimation is a crucial part of handling such probabilistic models …
Hyper-parameter Learning for Sparse Structured Probabilistic Models
In this paper, we consider the estimation of hyperparameters for regularization terms
commonly used for obtaining structured sparse parameters in signal estimation problems …
commonly used for obtaining structured sparse parameters in signal estimation problems …