sampling_independent

<p>a layer of tensors representing the mean and variance</p>

<p>A reparameterization in order to sample from the learned standard normal distribution of the VAE</p>

Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.

Geoffrey Converse

ML2Pvae

sampling_independent: A reparameterization in order to sample from the learned standard normal distribution of the VAE

Description

Usage

Arguments