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Weight tying in the context of an autoencoder refers to the practice of constraining the weights of the encoder and decoder parts of the autoencoder to be the same or somehow related. This can help to reduce the number of learnable parameters in the model, which in turn can make it easier to train and less prone to overfitting. Additionally, weight tying can help to enforce certain types of structure or symmetry on the learned representations of the data, which may be beneficial for certain types of tasks or applications.