The WAE model proposed in introduced a new family of regularized autoencoders under the name Wasserstein Autoencoders that minimize the optimal transport W c (p(x), p ′ (x)), where p(x) is the true data distribution and p ′ (x) is the generative model data distribution, for a cost function c. The WAE's regularizer, computed as part of the overall loss function, forces the continuous mixture q φ (z) = q φ (z|x)d p(x) to match the prior p(z) instead of forcing each input to match the prior as done in the VAE, where q φ (z|x = x i ) for all input i in x. The WAE constructed in this work, (Fig. ), uses the MMD penalty for prior regularization . The WAE implemented uses the same model as the RNN but exchanges the split latent space with a single linear layer. The regularization is performed in the loss function, where the inverse multi-quadratic kernel basis function is used and the MMD is computed between this latent vector and a vector with values sampled from a normal distribution.