EE/Columbia Medical School Seminar: Interpreting and Explaining Deep Neural Networks
Monday,
November 12, 2018
4:00 PM - 5:00 PM
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TwitterSpeaker:
Dr. Wojciech Samek
Head of Machine Learning Group
Department of Video Coding & Analytics
Fraunhofer Heinrich Hertz Institute HHI, Berlin, Germany
Abstract:
Deep neural networks (DNNs) are reaching or even exceeding the human level on an increasing number of complex tasks. However, due to their complex non-linear structure, these models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. This lack of transparency is a major drawback when applying DNNs to the sciences. In my talk I will present a general technique, Layer-wise Relevance Propagation (LRP), for interpreting DNNs by explaining their predictions. I will demonstrate the effectivity of LRP when applied to various datatypes (images, text, audio, video, EEG/fMRI signals) and neural architectures (ConvNets, LSTMs), and will summarize what we have learned so far by peering inside these black boxes.
Biography:
Dr. Wojciech Samek is head of the Machine Learning Group at Fraunhofer Heinrich Hertz Institute, Berlin, Germany. He studied Computer Science at Humboldt University of Berlin from 2004 to 2010 and received the PhD degree from the Technical University of Berlin in 2014. In 2009, he was visiting researcher at NASA Ames Research Center, Mountain View, CA. He organized workshops and tutorials about interpretable machine learning at various conferences, including CVPR, NIPS, ICASSP, MICCAI, ICIP, and received the best paper prize at the ICML'16 Workshop on Visualization for Deep Learning. He is part of the Focus Group on AI for Health, a world-wide initiative led by the ITU and WHO on the application of machine learning technology to the medical domain. He is associated with the Berlin Big Data Center and the Berlin Center of Machine Learning and is a member of the editorial board of Digital Signal Processing and PLOS ONE. His main research interest include interpretable and computationally efficient deep learning, computer vision and medical applications.
Dr. Wojciech Samek
Head of Machine Learning Group
Department of Video Coding & Analytics
Fraunhofer Heinrich Hertz Institute HHI, Berlin, Germany
Abstract:
Deep neural networks (DNNs) are reaching or even exceeding the human level on an increasing number of complex tasks. However, due to their complex non-linear structure, these models are usually applied in a black box manner, i.e., no information is provided about what exactly makes them arrive at their predictions. This lack of transparency is a major drawback when applying DNNs to the sciences. In my talk I will present a general technique, Layer-wise Relevance Propagation (LRP), for interpreting DNNs by explaining their predictions. I will demonstrate the effectivity of LRP when applied to various datatypes (images, text, audio, video, EEG/fMRI signals) and neural architectures (ConvNets, LSTMs), and will summarize what we have learned so far by peering inside these black boxes.
Biography:
Dr. Wojciech Samek is head of the Machine Learning Group at Fraunhofer Heinrich Hertz Institute, Berlin, Germany. He studied Computer Science at Humboldt University of Berlin from 2004 to 2010 and received the PhD degree from the Technical University of Berlin in 2014. In 2009, he was visiting researcher at NASA Ames Research Center, Mountain View, CA. He organized workshops and tutorials about interpretable machine learning at various conferences, including CVPR, NIPS, ICASSP, MICCAI, ICIP, and received the best paper prize at the ICML'16 Workshop on Visualization for Deep Learning. He is part of the Focus Group on AI for Health, a world-wide initiative led by the ITU and WHO on the application of machine learning technology to the medical domain. He is associated with the Berlin Big Data Center and the Berlin Center of Machine Learning and is a member of the editorial board of Digital Signal Processing and PLOS ONE. His main research interest include interpretable and computationally efficient deep learning, computer vision and medical applications.
Hosted By:
Shih-Fu Chang
Sr. Executive Vice Dean and Richard Dicker Professor Electrical Engineering and of Computer Science
Jiook Cha
Assistant Professor of Neurobiology, Department of Psychiatry, Columbia Medical School
Shih-Fu Chang
Sr. Executive Vice Dean and Richard Dicker Professor Electrical Engineering and of Computer Science
Jiook Cha
Assistant Professor of Neurobiology, Department of Psychiatry, Columbia Medical School
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