More concretely, the matrices Q c and S are initialized with I and E, which are identity matrices, and the Lipschitz constant: The underlying idea for reducing noise is to make use of a filter f to learn a denoising matrix Q c ; hence, we design the filter f as a non-linear, parameterized, feed-forward architecture with a fixed depth that can be trained to approximate f (X, Q c , S) to X as in Figure 2a.
noise from word embeddings will be filtered by layers of the filter f .