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Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes
Our approach models activation patterns of thresholded nodes in (deep) artificial neural networks as stochastic processes.
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on a new subjects data, and compare their performance to subject-specific models.
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