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Found 3 papers, 0 papers with code
Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features
In this paper, we propose a novel hierarchical likelihood learning framework for introducing gamma random effects into the Poisson DNN, so as to improve the prediction performance by capturing both nonlinear effects of input variables and subject-specific cluster effects.
Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
For prediction of clustered time-to-event data, we propose a new deep neural network based gamma frailty model (DNN-FM).
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
We believe that we can select a better reference frame to achieve the better UVOS performance than using only the first frame or the entire video as a reference frame.
Ranked #6 on
Unsupervised Video Object Segmentation
on FBMS test
