Description Usage Arguments Value Author(s) References See Also
Implementation of various loss functions to measure statistical discrepancy between two datasets.
1 2 3 4 
x 
2dtensor or (n, d)matrix (during training, n is the batch size and d is the dimension of the input dataset). 
y 
2dtensor or (m, d)matrix (during training, m is the batch size (and typically equal to n) and d is the dimension of the input dataset). 
type 

... 
additional arguments passed to the underlying functions,
most notably 
loss()
returns a 0d tensor containing the loss.
MMD()
and CvM()
return a 0d tensor (if x
and y
are tensors) or numeric(1)
(if x
or
y
are R matrices).
Marius Hofert and Avinash Prasad
Kingma, D. P. and Welling, M. (2014). Stochastic gradient VB and the variational autoencoder. Second International Conference on Learning Representations (ICLR). See https://keras.rstudio.com/articles/examples/variational_autoencoder.html
Rémillard, B. and Scaillet, O. (2009). Testing for equality between two copulas. Journal of Multivariate Analysis 100, 377–386.
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