Inst. Softwaretechnik und Theoretische Informatik

257 Items

Recent Submissions
Low dimensional visualization and modelling of data using distance-based models

Gruenhage, Gina (2018)

This thesis consists of two parts, which seek low-dimensional representations for visualization and analysis of data. Both parts use rather different types of models and inference methods. In both cases, however, the models show inherent invariances, which need to be coped with during the optimization procedures. The first part addresses a fundamental problem in machine learning, namely, the ch...

Benchmarking dataflow systems for scalable machine learning

Boden, Christoph (2018)

The popularity of the world wide web and its ubiquitous global online services have led to unprecedented amounts of available data. Novel distributed data processing systems have been developed in order to scale out computations and analysis to such massive data set sizes. These "Big Data Analytics" systems are also popular choices to scale out the execution of machine learning algorithms. Howe...

Formal verification of model refactorings for hybrid control systems

Schlesinger, Sebastian (2018)

The ever growing complexity in modern embedded systems require to incorporate increasingly many functions into a single system. Such increasing functionality leads to growing design complexity. Model Driven Engineering (MDE) has been proposed to improve the complexity management for development of embedded systems. An industrially widely used technique to reduce the complexity of models and est...

Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

Laskov, Pavel ; Schäfer, Christin ; Kotenko, Igor ; Müller, Klaus-Robert (2004)

The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlab...

A Neural Network Model for the Self-Organization of Cortical Grating Cells

Bauer, Christoph ; Burger, Thomas ; Stetter, Martin ; Lang, Elmar W. (2000)

A neural network model with incremental Hebbian learning of afferent and lateral synaptic couplings is proposed,which simulates the activity-dependent self-organization of grating cells in upper layers of striate cortex. These cells, found in areas V1 and V2 of the visual cortex of monkeys, respond vigorously and exclusively to bar gratings of a preferred orientation and periodicity. Response b...

Multimodal instrumentation and methods for neurotechnology out of the lab

Lühmann, Alexander von (2018)

In neuroscience and related fields, progress in instrumentation, computational power, and signal processing methods continuously provide novel and increasingly powerful tools toward the investigation of brain activity in real-time and everyday environments. Research into real-life and application-oriented, non-invasive neurotechnology bears a number of multidisciplinary challenges which need to...

Compiler assisted vulnerability assessment

Shastry, Bhargava (2018)

With computer software pervading every aspect of our lives, vulnerabilities pose an active threat. Moreover, with shorter software development cycles and a security-as-an-afterthought mindset, vulnerabilities in shipped code are inevitable. Therefore, recognizing and fixing vulnerabilities has gained in importance. At the same time, there is a demand for methods to diagnose vulnerabilities with...

Functional regression of densities with application to the simulation of molecular dynamics

Brockherde, Felix (2018)

Applications of machine learning have shown promising results modeling the non-interacting kinetic energy functional in 1-D. This holds the promise of enabling orbital-free density functional theory calculations, by-passing the computationally expensive Kohn-Sham equations. This would yield substantial savings in computer-time so that larger systems or longer time scales can be simulated. ...

Large-scale approximate EM-style learning and inference in generative graphical models for sparse coding

Shelton, Jacquelyn Ann (2018)

We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alternate between learning a `selection function' to reveal the relevant latent variables, and using this to obtain a compact approximation of the posterior distribution for EM; thi...

Data processing on heterogeneous hardware

Heimel, Max (2018)

The primary objective of data processing research on modern hardware is to understand how to utilize emerging technology to process data efficiently. Over the last decades, Software Engineers and Computer Scientists have made significant progress towards this goal, providing highly-tuned algorithms, systems & mechanisms for a wide variety of different device types. However, while we mostly unde...

Shaping the selection of fields of study in Afghanistan through educational data mining approaches

Sherzad, Abdul Rahman (2018)

Every year around 250000 high school graduates participate in ‘Kankor’, the Afghan national university entrance exam, while the seating capacity of the country’s 36 public universities is one-fourth of that number. Currently, public and private sectors lack advisory systems to guide the increasing number of participants to choose their fields of study. This is further exacerbated by the fact th...

Learning representations of atomistic systems with deep neural networks

Schütt, Kristof (2018)

Learning Representations of Atomistic Systems with Deep Neural Networks Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. However, with the rise of applying machine learning to quantum chemistry, research has been largely focused on the development of hand-crafted descriptors of atomistic systems. In this thesis, we propose novel n...

Lernen von Sensormesswerten zur Verbesserung von Fahrerassistenzsystemen am Beispiel eines adaptiven Kurvenwarners

Heinrichs, Robert (2018)

Today, Advanced Driver Assistance Systems are a central part of vehicles and improve safety and comfort. Especially in the safety area, Advanced Driver Assistance Systems have a significant positive impact, which is visible in accident statistics. With sensors and actuators Advanced Driver Assistance Systems can capture the vehicles state and its surroundings, and can intervene if necessary, s...

Fine-grained complexity analysis of some combinatorial data science problems

Froese, Vincent (2018)

This thesis is concerned with analyzing the computational complexity of NP-hard problems related to data science. For most of the problems considered in this thesis, the computational complexity has not been intensively studied before. We focus on the complexity of computing exact problem solutions and conduct a detailed analysis identifying tractable special cases. To this end, we adopt a para...

Burst-dependent bidirectional plasticity in the cerebellum Is driven by presynaptic NMDA receptors

Bouvier, Guy ; Higgins, David ; Spolidoro, Maria ; Carrel, Damien ; Mathieu, Benjamin ; Léna, Clément ; Dieudonné, Stéphane ; Barbour, Boris ; Brunel, Nicolas ; Casado, Mariano (2016-03-24)

Numerous studies have shown that cerebellar function is related to the plasticity at the synapses between parallel fibers and Purkinje cells. How specific input patterns determine plasticity outcomes, as well as the biophysics underlying plasticity of these synapses, remain unclear. Here, we characterize the patterns of activity that lead to postsynaptically expressed LTP using both in vivo and...

Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond

Sprekeler, Henning (2017-05-10)

Computational neuroscience has a long-standing tradition of investigating the consequences of excitatory synaptic plasticity. In contrast, the functions of inhibitory plasticity are still largely nebulous, particularly given the bewildering diversity of interneurons in the brain. Here, we review recent computational advances that provide first suggestions for the functional roles of inhibitory ...

Psychological needs as motivators for security and privacy actions on smartphones

Kraus, Lydia ; Wechsung, Ina ; Möller, Sebastian (2017-06)

Much work has been conducted to investigate the obstacles that keep users from using mitigations against security and privacy threats on smartphones. By contrast, we conducted in-depth interviews (N = 19) to explore users’ motivations for voluntarily applying security and privacy actions on smartphones. Our work focuses on analyzing intrinsic motivation in terms of psychological need fulfillmen...

Dorsolateral prefrontal cortex contributes to the impaired behavioral adaptation in alcohol dependence

Beylergil, Sinem Balta ; Beck, Anne ; Deserno, Lorenz ; Lorenz, Robert C. ; Rapp, Michael A. ; Schlagenhauf, Florian ; Heinz, Andreas ; Obermayer, Klaus (2017-04-17)

Substance-dependent individuals often lack the ability to adjust decisions flexibly in response to the changes in reward contingencies. Prediction errors (PEs) are thought to mediate flexible decision-making by updating the reward values associated with available actions. In this study, we explored whether the neurobiological correlates of PEs are altered in alcohol dependence. Behavioral, and ...

Explaining nonlinear classification decisions with deep Taylor decomposition

Montavon, Grégoire ; Lapuschkin, Sebastian ; Binder, Alexander ; Samek, Wojciech ; Müller, Klaus-Robert (2017-05)

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes du...

Machine learning of accurate energy-conserving molecular force fields

Chmiela, Stefan ; Tkatchenko, Alexandre ; Sauceda, Huziel E. ; Poltavsky, Igor ; Schütt, Kristof T. ; Müller, Klaus-Robert (2017)

Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of...