Interpretability in Deep Learning

Ayush Somani is a research fellow in the Department of Computer Science at UiT The Arctic University of Norway. He received his Integrated Masters from Indian Institute of Technology (ISM) Dhanbad in Maths and Computing. He has earned multiple honors like Dare2Compete Awards 2021, Samsung Innovation Award 2020, KDD'20 Travel Award, and Finalist in Machine Learning & Software Development Flipkart Grid 2.0 challenge. At Travel Buddy, he worked as a data scientist intern to implement AI-automated content moderation. He has research interests in interpretability, explanability, and other aspects of deep learning.  Alexander Horsch, born 1955, is a full professor at the Department of Computer Science, UiT The Arctic University of Norway. He holds a Ph.D. in Computer Science (1989) and a Dr. med. habil in Medical Informatics (1999), both from the Technical University of Munich (TUM). He was the head of the Medical Computing Center at Klinikum rechts der Isar, TUM (1987–1995), and researcher and lecturer, later associate professor and APL professor at TUM Medical Faculty (1996–2015). Several research projects in telemedicine and computer-aided diagnosis with grants from the German Ministry of Research and Technology, the Bavarian State Government, the European Union, and the German Telekom have been managed by him. From beginning 2015 to summer 2019, he was the head of the Department of Computer Science at UiT. He is a member of the research group for physical activity at UiT Medical Faculty, focusing on sensor data analysis within the Tromsø Study, a large epidemiological trial. He is the principal investigator of the interdisciplinary project VirtualStain (2020–2024) at UiT. Earlier, he has worked with the European Space Agency (ESA) since 2004 when he was a member of the ESA Telemed Working Group and with the World Health Organization (WHO) since 2005 as an eHealth and telemedicine expert. From 2006, he has worked in different periods as a consultant for EC (Telemedicine Task Force) and ESA in the Satellite-Enhanced eHealth for sub-Saharan Africa (eHSA) program. Since 2011, he was also supporting the United Nations Office for Outer Space Affairs (UNOOSA) in its Human Space Technology Initiative (HSTI). He is author or co-author of numerous scientific publications and has supervised a dozen doctoral students. His professional expertise ranges from eHealth applications to medical decision support. He has led or was a partner in projects for teleservices in gastroenterology and other medical specialties, web-based multi-modal interactive teaching of tumor diagnostics, case-based ophthalmologic eLearning, early detection of malignant melanoma, quantitative measurement of tumors using tomography data, and accelerometry for physical activity measurements in population studies and clinical research. His current scientific focus is on data analytics applied to biosensor time series and biological images using classical and machine learning approaches.  Dilip K. Prasad is an associate professor in the Department of Computer Science at UiT The Arctic University of Norway. He received the Ph.D. from Nanyang Technological University, Singapore and B.Tech. degree in Computer Science and Engineering from Indian Institute of Technology (ISM) Dhanbad, India. He was a senior research fellow at Nanyang Technological University, Singapore and research fellow at National University of Singapore. He has 5years of industrial experience with IBM, Infosys, Mediatek and Philips. He was a Kauffman Global Scholarship fellow in 2011. He has received 'Rolls-Royce Inventor Award' and several research grants from European Union, Research Council Norway and UiT The Arctic University of Norway. He is a founding member of Bio-AI Research Group at UiT The Arctic University of Norway.  His research interests include image processing, pattern recognition, computer vision and artificial intelligence. He is passionate about making artificial intelligence interpretable and scalable toward bridging the intelligence gap between human and machines.

• Chapter 1. Introduction.- Chapter 2. Neural networks for deep learning.- Chapter 3. Knowledge Encoding and Interpretation.- Chapter 4. Interpretation in Specific Deep Learning Architectures.- Chapter 5. Fuzzy Deep Learning.