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This lecture examines contrastive methods in energy-based models, their use in self-supervised learning, denoising autoencoders, regularized latent-variable models, sparse coding, and variational autoencoders. It also discusses ISTA, FISTA, and LISTA algorithms and filters learned from convolutional sparse encoders.
