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This problem is difficult because it is inherently unidentifiable.\nRecent approaches have shown that the identifiability problem could be reduced\nby introducing domain knowledge into the model. Deep neural networks have been\nshown to be a promising approach for these problems, but sliding windows are\nnecessary to handle the long sequences which arise in signal processing\nproblems, which raises issues about how to combine predictions from different\nsliding windows. In this paper, we propose sequence-to-point learning, where\nthe input is a window of the mains and the output is a single point of the\ntarget appliance. We use convolutional neural networks to train the model.\nInterestingly, we systematically show that the convolutional neural networks\ncan inherently learn the signatures of the target appliances, which are\nautomatically added into the model to reduce the identifiability problem. 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