Pruning by explaining: A novel criterion for deep neural network pruning
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Seul-Ki Yeom received a Ph.D. degree in Brain-Computer Interfacing from Korea University, in 2018. From 2018 to 2020, he was associated to the Machine Learning Group at Technische Universität Berlin. Since 2020, Seul-Ki holds a position as Senior Research Engineer at Nota.ai. His research interests include brain-computer interface, machine learning, and model compression.
Philipp Seegerer received a M.Sc. degree in Medical Image and Data Processing from Friedrich-Alexander-Universität Erlangen-Nürnberg, in 2017. He is currently a Doctoral Researcher in the Machine Learning Group at Technische Universität Berlin and since 2019 he is associated to Aignostics as a Machine Learning Engineer. His research interests are machine learning and medical image and data analysis, in particular computational pathology.
Sebastian Lapuschkin received an M.Sc. degree in Computer Science in 2013 and a Ph.D. degree from Technische Universität Berlin, in 2018. He is currently the Head of the Explainable AI Group at the Fraunhofer Heinrich Hertz Institute. His research interests are explainability and efficiciency in computer vision, machine learning and data analysis.
Alexander Binder obtained a Dr. rer. nat. degree from Technical University Berlin in 2013. He is currently an Associate Professor in the Institute of Informatics at the University of Oslo. He has been an Assistant Professor at SUTD from 2015 to 2020. His research interests include computer vision, machine learning, explaining non-linear predictions and medical applications.
Simon Wiedemann received a M.Sc. degree in applied mathematics from Technische Universität Berlin, in 2018. He is currently a Research Associate in the Department of Artificial Intelligence, Fraunhofer Heinrich-Hertz-Institute. His research interests include information theory and efficient machine learning, in particular compression, efficient inference and training of neural networks.
Klaus-Robert Müller (Ph.D. 92) has been a Professor of computer science at TU Berlin since 2006; co-director Berlin Big Data Center. He won the 1999 Olympus Prize of German Pattern Recognition Society, the 2006 SEL Alcatel Communication Award, and the 2014 Science Prize of Berlin. Since 2012, he is an elected member of the German National Academy of Sciences Leopoldina.
Wojciech Samek received a Diploma degree in Computer Science from Humboldt University Berlin in 2010 and the Ph.D. degree in Machine Learning from Technische Universität Berlin, in 2014. Currently, he directs the Department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute. His research interests include neural networks, interpretability and federated learning.