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GradMDM: Adversarial Attack on Dynamic Networks
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input.
Progressive Channel-Shrinking Network
Currently, salience-based channel pruning makes continuous breakthroughs in network compression.
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach
In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model.
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