masking_ratio: Numeric between 0 and 1. The probability for an entry in the generated mask to be 1 (masked).
paired_sampling: Boolean. If we are doing paired sampling. So include both S and Sˉ. If TRUE, then batch must be sampled using paired_sampler() which ensures that the batch contains two instances for each original observation. That is, batch=[X1,X1,X2,X2,X3,X3,...], where each entry Xj is a row of dimension p (i.e., the number of features).
Details
The mask generator mask each element in the batch (N x p) using a component-wise independent Bernoulli distribution with probability masking_ratio. Default values for masking_ratio is 0.5, so all masks are equally likely to be generated, including the empty and full masks. The function returns a mask of the same shape as the input batch, and the batch can contain missing values, indicated by the "NaN" token, which will always be masked.
Shape
Input: (N,p) where N is the number of observations in the batch and p is the number of features.