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52:42 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

Machine learning and applications

Since a few years Machine Learning (ML) has broadened the modeling toolbox for the sciences and industry. The talk will first remind the audience of the main ingredients for applying machine learning. Then various ML applications in the sciences namely Brain Computer Interfaces and Quantum Chemistry will be discussed.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
42:16 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

An introduction to inverse problems with applications in machine learning

The presentation starts with some motivating examples of Inverse Problems before introducing the general setting. We shortly review the most common regularization approaches (Tikhonov, iteration methods) and sketch some recent developments in sparsity and machine learning. Sparsity refers to additional expert information on the desired reconstruction, namely, that is has a finite expension in some predefined basis or frame. In machine learning we focus on 'multi colored' inverse problems, where part of the application can be formulated by a strict analytical framework but some part of the problem needs to modeled by a data driven approach. Those combined problems can be created by data- driven linear low rank approximations or more general black box models. In particular we review deep learning approaches to inverse problems. Finally, machine learning techniques by themselves are often inverse problems. We highlight basis learning techniques and applications to hyperspectral image analysis.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
27:38 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Lost in the Web?

To get information of mathematical software is sometimes like finding a needle in a haystack. But otherwise, more and more research results will be achieved by using mathematical software. Comprehensive and precise information about software used is also an important condition for the credibility of research results. But software has some unique features which pose new requirements to information infrastructure, especially the dynamic character of software, its encoding in formal languages, dependencies from hardware, other software, and further context information. The information on mathematical software is widely distributed on Web sites, repositories, portals, Web archives, etc., and is not standardized. The talk will address two relevant activities which can significantly improve the information about mathematical software: the initiatives for a citation standard for software, and the development of a comprehensive portal for mathematical software which integrates the existing information about software from the Web. A state of the art report on the swMATH service will illustrate the concepts and approaches.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
30:32 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Modeling decision tree training problems as a Mixed Integer Program (MIP) yields optimal decision trees

The problem of constructing an optimal binary decision tree is known to be NP-complete. Therefore most current implementations base on heuristics where local optimality criteria are used. Our approach optimizes decision trees globally by construction a suitable MIP. In recent years both very high computing power and very efficient branch-and-cut algorithms for solving MIPs make the running time more and more realistic for practical applications. We applied our method for the discrimination of tumor samples into distinct telomere maintenance mechanisms (TMM). Telomeres are at the end of the chromosomes and shorten after each replication serving as a crucial check point for protecting cells from unbound replication. Tumor cells circumvent this by either re-evoking the enzyme for elongation or redirecting DNA-repair mechanisms know as alternative TMM. Our approach allowed classifying the tumor samples with an accuracy of 0.95 when using the experimental gold standard (C-circle assays).
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
44:53 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) English 2017

Welcome/Opening MMS Days

  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: English
23:27 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

zbMATH beyond publications

Today, mathematical research information extends far beyond the classical publication format. This is especially true in the area of modelling and simulation, where the theoretical aspects of modelling are naturally connected with research data, mathematical software, and computational results. While all these components are essential in the process of research, they are not always similarly reflected in the publications. We describe how zbMATH currently supports the needs for documentation, information, and reputation management of research beyond texts. We also outline some approaches for future developments.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
16:44 Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB) German 2017

Combining linear Support Vector Machines by constraining them to use the same set of features improves consistency in biomarker discovery for blood infections

Blood infection is highly prevalent in critical ill patients and can lead to sepsis and often death. It can be caused by bacteria or fungi and for appropriate treatment it is mandatory to identify the type of infection early. To find discriminating biomarkers, in situ high throughput gene expression profiling of immune cells after fungal or bacterial infection have been performed. However, these studies showed very heterogeneous results. To find a generic gene signature with discriminative power across all datasets, we implemented linear SVMs basing on Mixed Integer Linear Programming. We combined classifiers constraining them to use the same set of features. Learning with one pair of datasets and applying to the rest of the datasets showed 43?mprovement in consistency of the selected features (genes) while non-decreased classification performance (accuracy: 0.96). The final biomarkers comprised of 19 genes mostly involved in ERK-MAPK signalling being central in immune response.
  • Published: 2017
  • Publisher: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS), Technische Informationsbibliothek (TIB)
  • Language: German
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