Publications | Max Planck Institute for Intelligent Systems

2019

ics Haksar, R., Solowjow, F., Trimpe, S., Schwager, M. Controlling Heterogeneous Stochastic Growth Processes on Lattices with Limited Resources In Proceedings of the 58th IEEE International Conference on Decision and Control (CDC) , 58th IEEE International Conference on Decision and Control (CDC), December 2019 (proceedings) Accepted

Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems

Baumann, D.

KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

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ics Baumann, D. Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems KTH Royal Institute of Technology, Stockholm, Febuary 2019 (phdthesis)

Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

ei Rojas-Carulla, M. Learning Transferable Representations University of Cambridge, UK, 2019 (phdthesis)

Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

ei Gu, S. Sample-efficient deep reinforcement learning for continuous control University of Cambridge, UK, 2019 (phdthesis)

Formally justified and modular Bayesian inference for probabilistic programs

Ścibior*, A.

University of Cambridge, UK, 2019 (phdthesis)

ei Ścibior*, A. Formally justified and modular Bayesian inference for probabilistic programs University of Cambridge, UK, 2019 (phdthesis)

Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

ei Xu, J. Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing Technical University of Munich, Germany, 2019 (mastersthesis)

Pragmatism and Variable Transformations in Causal Modelling

Weichwald, S.

ETH Zurich, 2019 (phdthesis)

ei Weichwald, S. Pragmatism and Variable Transformations in Causal Modelling ETH Zurich, 2019 (phdthesis)

Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

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mms Sanli, U. T. Novel X-ray lenses for direct and coherent imaging Universität Stuttgart, Stuttgart, 2019 (phdthesis)

Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

ei Katiyar, P. Quantification of tumor heterogeneity using PET/MRI and machine learning Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning

Bauer, M.

University of Cambridge, UK, 2019 (phdthesis)

ei Bauer, M. Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning University of Cambridge, UK, 2019 (phdthesis)

Actively Learning Dynamical Systems with Gaussian Processes

Buisson-Fenet, M.

Mines ParisTech, PSL Research University, 2019 (mastersthesis)

Abstract

Predicting the behavior of complex systems is of great importance in many fields such as engineering, economics or meteorology. The evolution of such systems often follows a certain structure, which can be induced, for example from the laws of physics or of market forces. Mathematically, this structure is often captured by differential equations. The internal functional dependencies, however, are usually unknown. Hence, using machine learning approaches that recreate this structure directly from data is a promising alternative to designing physics-based models. In particular, for high dimensional systems with nonlinear effects, this can be a challenging task. Learning dynamical systems is different from the classical machine learning tasks, such as image processing, and necessitates different tools. Indeed, dynamical systems can be actuated, often by applying torques or voltages. Hence, the user has a power of decision over the system, and can drive it to certain states by going through the dynamics. Actuating this system generates data, from which a machine learning model of the dynamics can be trained. However, gathering informative data that is representative of the whole state space remains a challenging task. The question of active learning then becomes important: which control inputs should be chosen by the user so that the data generated during an experiment is informative, and enables efficient training of the dynamics model? In this context, Gaussian processes can be a useful framework for approximating system dynamics. Indeed, they perform well on small and medium sized data sets, as opposed to most other machine learning frameworks. This is particularly important considering data is often costly to generate and process, most of all when producing it involves actuating a complex physical system. Gaussian processes also yield a notion of uncertainty, which indicates how sure the model is about its predictions. In this work, we investigate in a principled way how to actively learn dynamical systems, by selecting control inputs that generate informative data. We model the system dynamics by a Gaussian process, and use information-theoretic criteria to identify control trajectories that maximize the information gain. Thus, the input space can be explored efficiently, leading to a data-efficient training of the model. We propose several methods, investigate their theoretical properties and compare them extensively in a numerical benchmark. The final method proves to be efficient at generating informative data. Thus, it yields the lowest prediction error with the same amount of samples on most benchmark systems. We propose several variants of this method, allowing the user to trade off computations with prediction accuracy, and show it is versatile enough to take additional objectives into account.

ics

ics Buisson-Fenet, M. Actively Learning Dynamical Systems with Gaussian Processes Mines ParisTech, PSL Research University, 2019 (mastersthesis)

Official Version [BibTex]

2017

Human Shape Estimation using Statistical Body Models

Loper, M. M.

University of Tübingen, May 2017 (thesis)

Abstract

Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance. Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. We address this challenge with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates. Body state inference requires more than a body model; we therefore build obser- vation models whose output is compared with real observations. In this thesis, body state is estimated from three types of observations: 3D motion capture markers, depth and color images, and high-resolution 3D scans. In each case, a forward process is proposed which simulates observations. By comparing observations to the results of the forward process, state can be adjusted to minimize the difference between simulated and observed data. We use gradient-based methods because they are critical to the precise estimation of state with a large number of parameters. The contributions of this work include three parts. First, we propose a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers. Second, we present a concise approach to differentiating through the rendering process, with application to body shape estimation. And finally, we present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages.

2017

ps Loper, M. M. Human Shape Estimation using Statistical Body Models University of Tübingen, May 2017 (thesis)

Official Version [BibTex]

Nonparametric Disturbance Correction and Nonlinear Dual Control

Klenske, E. D.

(24098), ETH Zurich, 2017 (phdthesis)

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DOI [BibTex]

ei pn sf Klenske, E. D. Nonparametric Disturbance Correction and Nonlinear Dual Control (24098), ETH Zurich, 2017 (phdthesis)

DOI [BibTex]

Learning Inference Models for Computer Vision

Jampani, V.

MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

Abstract

Computer vision can be understood as the ability to perform 'inference' on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques, as even the model design is often dictated by the complexity of inference in them. This thesis proposes learning based inference schemes and demonstrates applications in computer vision. We propose techniques for inference in both generative and discriminative computer vision models. Despite their intuitive appeal, the use of generative models in vision is hampered by the difficulty of posterior inference, which is often too complex or too slow to be practical. We propose techniques for improving inference in two widely used techniques: Markov Chain Monte Carlo (MCMC) sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative vision models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge in a principled way. For better inference in discriminative models, we propose techniques that modify the original model itself, as inference is simple evaluation of the model. We concentrate on convolutional neural network (CNN) models and propose a generalization of standard spatial convolutions, which are the basic building blocks of CNN architectures, to bilateral convolutions. First, we generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. We show how the bilateral filtering modules can be used for modifying existing CNN architectures for better image segmentation and propose a neural network approach for temporal information propagation in videos. Experiments demonstrate the potential of the proposed bilateral networks on a wide range of vision tasks and datasets. In summary, we propose learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks. In generative vision models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way for incorporating prior knowledge into CNNs.

ps Jampani, V. Learning Inference Models for Computer Vision MPI for Intelligent Systems and University of Tübingen, 2017 (phdthesis)

Development and Evaluation of a Portable BCI System for Remote Data Acquisition

Emde, T.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

ei Emde, T. Development and Evaluation of a Portable BCI System for Remote Data Acquisition Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis

Fomina, T.

Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

ei Fomina, T. Brain-Computer Interfaces for patients with Amyotrophic Lateral Sclerosis Eberhard Karls Universität Tübingen, Germany, 2017 (phdthesis)

Causal models for decision making via integrative inference

Geiger, P.

University of Stuttgart, Germany, 2017 (phdthesis)

ei Geiger, P. Causal models for decision making via integrative inference University of Stuttgart, Germany, 2017 (phdthesis)

Thesis link (url) Project Page [BibTex]

Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Tzionas, D.

University of Bonn, 2017 (phdthesis)

Abstract

Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object's shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow.

ps Tzionas, D. Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects University of Bonn, 2017 (phdthesis)

Thesis link (url) Project Page [BibTex]

Evaluation of the passive dynamics of compliant legs with inertia

Györfi, B.

University of Applied Science Pforzheim, Germany, 2017 (mastersthesis)

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dlg Györfi, B. Evaluation of the passive dynamics of compliant legs with inertia University of Applied Science Pforzheim, Germany, 2017 (mastersthesis)

Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation

Sücker, K.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

ei Sücker, K. Learning Optimal Configurations for Modeling Frowning by Transcranial Electrical Stimulation Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2017 (mastersthesis)

Understanding FORC using synthetic micro-structured systems with variable coupling- and coercivefield distributions

Groß, Felix

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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mms Groß, Felix Understanding FORC using synthetic micro-structured systems with variable coupling- and coercivefield distributions Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

Using FORC to understand the microstructure-micromagnetism relationship in supermagnets

Ilse, S. E.

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

mms

mms Ilse, S. E. Using FORC to understand the microstructure-micromagnetism relationship in supermagnets Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

Adsorption von Wasserstoffmolekülen in nanoporösen Gerüststrukturen

Kotzur, Nadine

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

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mms Kotzur, Nadine Adsorption von Wasserstoffmolekülen in nanoporösen Gerüststrukturen Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

Tailored magnetic properties of exchange-spring and ultra hard nanomagnets

Son, K.

Universität Stuttgart, Stuttgart, 2017 (phdthesis)

mms

mms Son, K. Tailored magnetic properties of exchange-spring and ultra hard nanomagnets Universität Stuttgart, Stuttgart, 2017 (phdthesis)

Voltage induced near interface changes of the magnetocrystalline anisotropy energy: A study by X-ray resonant techniques in combination with conventional magnetometry

Boltje, D.

Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

mms

mms Boltje, D. Voltage induced near interface changes of the magnetocrystalline anisotropy energy: A study by X-ray resonant techniques in combination with conventional magnetometry Universität Stuttgart, Stuttgart, 2017 (mastersthesis)

2016

Non-parametric Models for Structured Data and Applications to Human Bodies and Natural Scenes

Lehrmann, A.

ETH Zurich, July 2016 (phdthesis)

Abstract

The purpose of this thesis is the study of non-parametric models for structured data and their fields of application in computer vision. We aim at the development of context-sensitive architectures which are both expressive and efficient. Our focus is on directed graphical models, in particular Bayesian networks, where we combine the flexibility of non-parametric local distributions with the efficiency of a global topology with bounded treewidth. A bound on the treewidth is obtained by either constraining the maximum indegree of the underlying graph structure or by introducing determinism. The non-parametric distributions in the nodes of the graph are given by decision trees or kernel density estimators. The information flow implied by specific network topologies, especially the resultant (conditional) independencies, allows for a natural integration and control of contextual information. We distinguish between three different types of context: static, dynamic, and semantic. In four different approaches we propose models which exhibit varying combinations of these contextual properties and allow modeling of structured data in space, time, and hierarchies derived thereof. The generative character of the presented models enables a direct synthesis of plausible hypotheses. Extensive experiments validate the developed models in two application scenarios which are of particular interest in computer vision: human bodies and natural scenes. In the practical sections of this work we discuss both areas from different angles and show applications of our models to human pose, motion, and segmentation as well as object categorization and localization. Here, we benefit from the availability of modern datasets of unprecedented size and diversity. Comparisons to traditional approaches and state-of-the-art research on the basis of well-established evaluation criteria allows the objective assessment of our contributions.

2016

ps Lehrmann, A. Non-parametric Models for Structured Data and Applications to Human Bodies and Natural Scenes ETH Zurich, July 2016 (phdthesis)

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI)

Ihler, A. T., Janzing, D.

pages: 869, AUAI Press, June 2016 (proceedings)

ei Ihler, A. T., Janzing, D. Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence (UAI) pages: 869, AUAI Press, June 2016 (proceedings)

Advances in computational imaging: Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields

Köhler, R.

Eberhard Karls Universität Tübingen, Germany, 2016 (phdthesis)

ei Köhler, R. Advances in computational imaging: Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields Eberhard Karls Universität Tübingen, Germany, 2016 (phdthesis)

Tractable Structured Prediction using the Permutohedral Lattice

Kiefel, M.

ETH Zurich, 2016 (phdthesis)

ei Kiefel, M. Tractable Structured Prediction using the Permutohedral Lattice ETH Zurich, 2016 (phdthesis)

Untersuchung des Beitrages von Elektron-Magnon Streuungen zur ultraschnellen Entmagnetisierung nach Femtosekunden Laserbestrahlung ferromagnetischer Filme

Haag, M.

Universität Stuttgart, Stuttgart, 2016 (phdthesis)

mms

mms Haag, M. Untersuchung des Beitrages von Elektron-Magnon Streuungen zur ultraschnellen Entmagnetisierung nach Femtosekunden Laserbestrahlung ferromagnetischer Filme Universität Stuttgart, Stuttgart, 2016 (phdthesis)

Statische und dynamische Magnetisierungseigenschaften nanoskaliger Überstrukturen

Gräfe, J.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

mms Gräfe, J. Statische und dynamische Magnetisierungseigenschaften nanoskaliger Überstrukturen Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

Gepinnte Bahnmomente in magnetischen Heterostrukturen

Audehm, P.

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

mms Audehm, P. Gepinnte Bahnmomente in magnetischen Heterostrukturen Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

Austauschgekoppelte Moden in magnetischen Vortexstrukturen

Dieterle, G.

Universität Stuttgart, Stuttgart, 2016 (phdthesis)

mms

mms Dieterle, G. Austauschgekoppelte Moden in magnetischen Vortexstrukturen Universität Stuttgart, Stuttgart, 2016 (phdthesis)

Density matrix calculations for the ultrafast demagnetization after femtosecond laser pulses

Weng, Weikai

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

mms

mms Weng, Weikai Density matrix calculations for the ultrafast demagnetization after femtosecond laser pulses Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

Electric field manipulation of perpendicular magnetic anisotropy in microstructured Cobalt-Iron-Boron Magnesium Oxide thin film systems

Bauch, J.

Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

mms

mms Bauch, J. Electric field manipulation of perpendicular magnetic anisotropy in microstructured Cobalt-Iron-Boron Magnesium Oxide thin film systems Universität Stuttgart, Stuttgart, 2016 (mastersthesis)

Deep Learning for Diabetic Retinopathy Diagnostics

Balles, Lukas

Heidelberg University, 2016 (mastersthesis)

Balles, Lukas Deep Learning for Diabetic Retinopathy Diagnostics Heidelberg University, 2016 (mastersthesis)

Helium und Hydrogen Isotope Adsorption and Separation in Metal-Organic Frameworks

Zaiser, Ingrid

Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

mms

mms Zaiser, Ingrid Helium und Hydrogen Isotope Adsorption and Separation in Metal-Organic Frameworks Universität Stuttgart, Stuttgart (und Cuvillier Verlag, Göttingen), 2016 (phdthesis)

2014

Pole Balancing with Apollo

Holger Kaden

Eberhard Karls Universität Tübingen, December 2014 (mastersthesis)

2014

am Holger Kaden Pole Balancing with Apollo Eberhard Karls Universität Tübingen, December 2014 (mastersthesis)

Modeling the polygenic architecture of complex traits

Rakitsch, Barbara

Eberhard Karls Universität Tübingen, November 2014 (phdthesis)

ei Rakitsch, Barbara Modeling the polygenic architecture of complex traits Eberhard Karls Universität Tübingen, November 2014 (phdthesis)

Learning Coupling Terms for Obstacle Avoidance

Rai, A.

École polytechnique fédérale de Lausanne, August 2014 (mastersthesis)

am Rai, A. Learning Coupling Terms for Obstacle Avoidance École polytechnique fédérale de Lausanne, August 2014 (mastersthesis)

Object Tracking in Depth Images Using Sigma Point Kalman Filters

Issac, J.

Karlsruhe Institute of Technology, July 2014 (mastersthesis)

am Issac, J. Object Tracking in Depth Images Using Sigma Point Kalman Filters Karlsruhe Institute of Technology, July 2014 (mastersthesis)

Modeling the Human Body in 3D: Data Registration and Human Shape Representation

Tsoli, A.

Brown University, Department of Computer Science, May 2014 (phdthesis)

ps Tsoli, A. Modeling the Human Body in 3D: Data Registration and Human Shape Representation Brown University, Department of Computer Science, May 2014 (phdthesis)

Probabilistic Object Tracking on the GPU Project Page [BibTex]

Probabilistic Object Tracking on the GPU

Pfreundt, C.

Karlsruhe Institute of Technology, March 2014 (mastersthesis)

am Pfreundt, C. Probabilistic Object Tracking on the GPU Karlsruhe Institute of Technology, March 2014 (mastersthesis)

Probabilistic Object Tracking on the GPU Project Page [BibTex]

A Novel Causal Inference Method for Time Series

Shajarisales, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)

ei Shajarisales, N. A Novel Causal Inference Method for Time Series Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (mastersthesis)

A global analysis of extreme events and consequences for the terrestrial carbon cycle

Zscheischler, J.

Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

ei Zscheischler, J. A global analysis of extreme events and consequences for the terrestrial carbon cycle Diss. No. 22043, ETH Zurich, Switzerland, ETH Zurich, Switzerland, 2014 (phdthesis)

Project Page Project Page [BibTex]

Learning objective functions for autonomous motion generation

Kalakrishnan, M.

University of Southern California, University of Southern California, Los Angeles, CA, 2014 (phdthesis)

am Kalakrishnan, M. Learning objective functions for autonomous motion generation University of Southern California, University of Southern California, Los Angeles, CA, 2014 (phdthesis)

Project Page Project Page [BibTex]

Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

ei Schmeißer, N. Development of advanced methods for improving astronomical images Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection

Lacosse, E.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

ei Lacosse, E. The Feasibility of Causal Discovery in Complex Systems: An Examination of Climate Change Attribution and Detection Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size

Huang, B.

Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

ei Huang, B. Causal Discovery in the Presence of Time-Dependent Relations or Small Sample Size Graduate Training Centre of Neuroscience, University of Tübingen, Germany, Graduate Training Centre of Neuroscience, University of Tübingen, Germany, 2014 (mastersthesis)

Analysis of Distance Functions in Graphs

Alamgir, M.

University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)

ei Alamgir, M. Analysis of Distance Functions in Graphs University of Hamburg, Germany, University of Hamburg, Germany, 2014 (phdthesis)