The following ideas have been implemented and compared by us
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Using Hamiltonian MC sampling instead of standard methods to improve performance and accuracy when sampling from the model
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Generating continuous embeddings using VAEs for discrete variables in the latent space to train the diffusion models with
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Exploiting the sparsity of data using signal processing techniques like wavelet transformations