Deep Learning
Freitag, 2.6.17, 12:00-13:00, Raum 404, Eckerstr. 1
Deep learning has been getting large attention in science and the media. In my talk I will show results mainly from computer vision that explain this attention and indicates how things could go on in the future. The talk will consist of three main parts. In the first part, I will give a brief introduction into the fundamentals of deep learning, such as common network architectures and the basic back-propagation algorithm for optimization of their parameters. In the second part, I will show recent results from my group, which developed for the first time learning formulations for 3D computer vision. In the third part, I will list mathematical challenges in deep learning, the solution of which would probably largely improve the state of the art.
(Localized) learning with kernels
Freitag, 16.6.17, 12:00-13:00, Raum 404, Eckerstr. 1
Using reproducing kernel Hilbert spaces in non-parametric approaches\nfor regression and classification has a long-standing history. In\nthe first part of this talk, I will introduce these kernel-based learning\n(KBL) methods and discuss some existing statistical guarantees for them.\nIn the second part I will present a localization approach that addresses\nthe super-linear computational requirements of KBLs in terms of the number\nof training samples. I will further provide a statistical analysis that\nshows that the "local KBL" achieves the same learning rates as the original,\nglobal KBL. Furthermore, I will report from some large scale experiments\nshowing that the local KBL achieves essentially the same test performance\nas the global KBL, but for a fraction of the computational requirements.\nIn addition, it turns out that the computational requirements for the local\nKBLs are similar to those of a vanilla random chunk approach, while the\nachieved test errors are in most cases significantly better. Finally, if time\npermits, I will briefly explain, how these methods are being made available\nin a recent software package.
Computational Models as Drivers of Cardiac Research
Freitag, 30.6.17, 12:00-13:00, Raum 404, Eckerstr. 1
What are models? What is their role in biological research? Can they be relied on? Can computer simulations replace experiments on living animals? When will we have an all-inclusive model of [...insert system of choice...]? Questions like this are frequently raised in professional and lay discussions. This lecture will attempt to address some aspects, using illustrations 'from the heart'.\n