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Least-Squares Based Layerwise Pruning Of Convolutional Neural Networks., and . SSP, page 60-64. IEEE, (2018)A new approach for supervised power disaggregation by using a deep recurrent LSTM network, and . Proceedings of the 3rd IEEE Global Conference on Signal and Information Processing GlobalSIP 2015, March 2015, Orlando, Florida, page 63-67. Piscataway, NJ, IEEE, (2015)Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging, , , , , , , , , and . Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2018)Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Motion Artifact Detection, , , , , , , , , and 3 other author(s). Proceedings of the IEEE Asia-Pacific Signal and Information Processing Association (APSIPA), (2018)Deep Neural Network inference with reduced word length., and . CoRR, (2018)Differentiable Quantization of Deep Neural Networks., , , , , , , and . CoRR, (2019)Iteratively Training Look-Up Tables for Network Quantization., , , , , , , and . CoRR, (2019)Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging, , , , , , , , , and . 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), page 995-999. Piscataway, NJ, IEEE, (2018)Automated reference-free detection of motion artifacts in magnetic resonance images, , , , , , , , and . Magnetic Resonance Materials in Physics Biology and Medicine, 31 (2): 243-256 (2018)A Fully Automatic Reference Deconvolution Strategy to Increase the Accuracy of In Vivo Lipid Signal Quantification, , , , and . Magnetic resonance in medicine, 75 (4): 1391-1401 (2016)