| Universität Paderborn, L2
Am Montag, 25. Juni 2018, hält Prof. Dr. Volker Tresp vom Lehrstuhl für Datenbanksysteme und Data Mining an der Ludwig-Maximilians-Universität München das Fakultätskolloquium mit dem Titel Deep X: Deep Learning with Deep Knowledge.
Abstract: Labeled graphs can describe states and events at a cognitive abstraction level, representing facts as subject-predicate-object triples. A prominent and very successful example is the Google Knowledge Graph, representing on the order of 100B facts. Labeled graphs can be represented as adjacency tensors which can serve as inputs for prediction and decision making, and from which tensor models can be derived to generalize to unseen facts. We show how these ideas can be used, together with deep recurrent networks, for clinical decision support by predicting orders and outcomes. Following Goethe’s proverb, “you only see what you know”, we show how background knowledge can dramatically improve information extraction from images by deep convolutional networks and how tensor train models can be used for the efficient classification of videos. We discuss potential links to the memory and perceptual systems of the human brain. We conclude that tensor models, in connection with deep learning, can be the basis for many technical solutions requiring memory and perception, and might be a basis for modern AI.
CV: Volker Tresp received a Diploma degree from the University of Goettingen, Germany, in 1984 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively. Since 1989 he is the head of various research teams in machine learning at Siemens, Research and Technology. He filed more than 70 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 20 Ph.D. theses. The company Panoratio is a spin-off out of his team. His research focus in recent years has been „Machine Learning in Information Networks“ for modelling Knowledge Graphs, medical decision processes and sensor networks. He is the coordinator of one of the first nationally funded Big Data projects for the realization of „Precision Medicine“. Since 2011 he is also a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.