Institute of Electronics, Information and Communication Engineers. Engineering Sciences Society. Technical Committee on Information Security, Information Processing Society of Japan. Special Interest Group on Computer Security, and International Workshop on Security (15th : 2020 : Online)
Computer security--Congresses, Sécurité informatique--Congrès, Computer networking & communications, Information technology: general issues, Artificial intelligence, Software Engineering, Computer security, Computers--Networking--General, Computers--Online Services--General, Computers--Social Aspects--Human-Computer Interaction, Computers--Intelligence (AI) & Semantics, Computers--Software Development & Engineering--General, Computers--Security--General, and QA76.9.A25
Murayama, Yuko, Velev, Dimiter, Zlateva, Plamena, International Federation for Information Processing. Domain Committee on Information Technology in Disaster Risk Reduction, and IFIP Conference on Information Technology in Disaster Risk Reduction (4th : 2019 : Kyiv, Ukraine)
Emergency management--Data processing--Congresses, Natural disasters--Data processing--Congresses, Coding theory & cryptology, Network hardware, Artificial intelligence, Information retrieval, Computers--Information Theory, Computers--Hardware--Network Hardware, Computers--Intelligence (AI) & Semantics, Computers--Information Technology, 353.9/50285, and HV551.2
Causo, Albert, Durham, Joseph (Joseph William), Hauser, Kris (College teacher), Okada, Kei (College teacher), Rodriguez, Alberto (Professor of mechanical engineering), and Albert Causo, Joseph Durham, Kris Hauser, Kei Okada, Alberto Rodriguez, editors
The goal of this book is to provide a mathematical perspective on some key elements of the so-called deep neural networks (DNNs). Much of the interest in deep learning has focused on the implementation of DNN-based algorithms. Our hope is that this compact textbook will offer a complementary point of view that emphasizes the underlying mathematical ideas. We believe that a more foundational perspective will help to answer important questions that have only received empirical answers so far. The material is based on a one-semester course Introduction to Mathematics of Deep Learning' for senior undergraduate mathematics majors and first year graduate students in mathematics. Our goal is to introduce basic concepts from deep learning in a rigorous mathematical fashion, e.g introduce mathematical definitions of deep neural networks (DNNs), loss functions, the backpropagation algorithm, etc. We attempt to identify for each concept the simplest setting that minimizes technicalities but still contains the key mathematics.
Murakami, Yohei, Lin, Donghui, Kyoto University. Department of Social Informatics. Ishida and Matsubara Laboratory. Language Grid Project, and WLSI (Workshop)
Natural language processing (Computer science)--Congresses, Computer Science, Artificial Intelligence (incl. Robotics), Information Systems Applications (incl. Internet), Language Translation and Linguistics, User Interfaces and Human Computer Interaction, Information Storage and Retrieval, Computational Linguistics, Computers--Information Technology, Computers--Speech & Audio Processing, Computers--User Interfaces, Computers--System Administration--Storage & Retrieval, Language Arts & Disciplines--Linguistics--General, Information retrieval, Natural language & machine translation, User interface design & usability, Computational linguistics, Computers--Intelligence (AI) & Semantics, Artificial intelligence, 006.3/5, and QA76.9.N38