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2019


Towards Geometric Understanding of Motion
Towards Geometric Understanding of Motion

Ranjan, A.

University of Tübingen, December 2019 (phdthesis)

Abstract

The motion of the world is inherently dependent on the spatial structure of the world and its geometry. Therefore, classical optical flow methods try to model this geometry to solve for the motion. However, recent deep learning methods take a completely different approach. They try to predict optical flow by learning from labelled data. Although deep networks have shown state-of-the-art performance on classification problems in computer vision, they have not been as effective in solving optical flow. The key reason is that deep learning methods do not explicitly model the structure of the world in a neural network, and instead expect the network to learn about the structure from data. We hypothesize that it is difficult for a network to learn about motion without any constraint on the structure of the world. Therefore, we explore several approaches to explicitly model the geometry of the world and its spatial structure in deep neural networks.

The spatial structure in images can be captured by representing it at multiple scales. To represent multiple scales of images in deep neural nets, we introduce a Spatial Pyramid Network (SpyNet). Such a network can leverage global information for estimating large motions and local information for estimating small motions. We show that SpyNet significantly improves over previous optical flow networks while also being the smallest and fastest neural network for motion estimation. SPyNet achieves a 97% reduction in model parameters over previous methods and is more accurate.

The spatial structure of the world extends to people and their motion. Humans have a very well-defined structure, and this information is useful in estimating optical flow for humans. To leverage this information, we create a synthetic dataset for human optical flow using a statistical human body model and motion capture sequences. We use this dataset to train deep networks and see significant improvement in the ability of the networks to estimate human optical flow.

The structure and geometry of the world affects the motion. Therefore, learning about the structure of the scene together with the motion can benefit both problems. To facilitate this, we introduce Competitive Collaboration, where several neural networks are constrained by geometry and can jointly learn about structure and motion in the scene without any labels. To this end, we show that jointly learning single view depth prediction, camera motion, optical flow and motion segmentation using Competitive Collaboration achieves state-of-the-art results among unsupervised approaches.

Our findings provide support for our hypothesis that explicit constraints on structure and geometry of the world lead to better methods for motion estimation.

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PhD Thesis [BibTex]

2019


PhD Thesis [BibTex]


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Robot Learning for Muscular Robots

Büchler, D.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Real Time Probabilistic Models for Robot Trajectories

Gomez-Gonzalez, S.

Technical University Darmstadt, Germany, December 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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

Baumann, D.

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

ics

PDF [BibTex]

PDF [BibTex]


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Learning Transferable Representations

Rojas-Carulla, M.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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Sample-efficient deep reinforcement learning for continuous control

Gu, S.

University of Cambridge, UK, 2019 (phdthesis)

ei

[BibTex]


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X-ray microscopic characterization of high-Tc-supercoductors using image processing

Bihler, M.

Universität Stuttgart, Stuttgart, 2019 (mastersthesis)

mms

[BibTex]


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Spatial Filtering based on Riemannian Manifold for Brain-Computer Interfacing

Xu, J.

Technical University of Munich, Germany, 2019 (mastersthesis)

ei

[BibTex]

[BibTex]


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Novel X-ray lenses for direct and coherent imaging

Sanli, U. T.

Universität Stuttgart, Stuttgart, 2019 (phdthesis)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Quantification of tumor heterogeneity using PET/MRI and machine learning

Katiyar, P.

Eberhard Karls Universität Tübingen, Germany, 2019 (phdthesis)

ei

[BibTex]

[BibTex]


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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

[BibTex]

[BibTex]

2009


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Machine Learning for Brain-Computer Interfaces

Hill, NJ.

Mini-Symposia on Assistive Machine Learning for People with Disabilities at NIPS (AMD), December 2009 (talk)

Abstract
Brain-computer interfaces (BCI) aim to be the ultimate in assistive technology: decoding a user‘s intentions directly from brain signals without involving any muscles or peripheral nerves. Thus, some classes of BCI potentially offer hope for users with even the most extreme cases of paralysis, such as in late-stage Amyotrophic Lateral Sclerosis, where nothing else currently allows communication of any kind. Other lines in BCI research aim to restore lost motor function in as natural a way as possible, reconnecting and in some cases re-training motor-cortical areas to control prosthetic, or previously paretic, limbs. Research and development are progressing on both invasive and non-invasive fronts, although BCI has yet to make a breakthrough to widespread clinical application. The high-noise high-dimensional nature of brain-signals, particularly in non-invasive approaches and in patient populations, make robust decoding techniques a necessity. Generally, the approach has been to use relatively simple feature extraction techniques, such as template matching and band-power estimation, coupled to simple linear classifiers. This has led to a prevailing view among applied BCI researchers that (sophisticated) machine-learning is irrelevant since "it doesn‘t matter what classifier you use once you‘ve done your preprocessing right and extracted the right features." I shall show a few examples of how this runs counter to both the empirical reality and the spirit of what needs to be done to bring BCI into clinical application. Along the way I‘ll highlight some of the interesting problems that remain open for machine-learners.

ei

PDF Web Web [BibTex]

2009


PDF Web Web [BibTex]


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PAC-Bayesian Approach to Formulation of Clustering Objectives

Seldin, Y.

NIPS Workshop on "Clustering: Science or Art? Towards Principled Approaches", December 2009 (talk)

Abstract
Clustering is a widely used tool for exploratory data analysis. However, the theoretical understanding of clustering is very limited. We still do not have a well-founded answer to the seemingly simple question of "how many clusters are present in the data?", and furthermore a formal comparison of clusterings based on different optimization objectives is far beyond our abilities. The lack of good theoretical support gives rise to multiple heuristics that confuse the practitioners and stall development of the field. We suggest that the ill-posed nature of clustering problems is caused by the fact that clustering is often taken out of its subsequent application context. We argue that one does not cluster the data just for the sake of clustering it, but rather to facilitate the solution of some higher level task. By evaluation of the clustering‘s contribution to the solution of the higher level task it is possible to compare different clusterings, even those obtained by different optimization objectives. In the preceding work it was shown that such an approach can be applied to evaluation and design of co-clustering solutions. Here we suggest that this approach can be extended to other settings, where clustering is applied.

ei

PDF Web Web [BibTex]

PDF Web Web [BibTex]


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Semi-supervised Kernel Canonical Correlation Analysis of Human Functional Magnetic Resonance Imaging Data

Shelton, JA.

Women in Machine Learning Workshop (WiML), December 2009 (talk)

Abstract
Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, KCCA may suffer from small sample effects. We propose to use semi-supervised Laplacian regularization to utilize data that are present in only one modality. This manifold learning approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned and such data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of KCCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, Laplacian regularization improved performance whereas the semi-supervised variants of KCCA yielded the best performance. We additionally analyze the weights learned by the regression in order to infer brain regions that are important during different types of visual processing.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Event-Related Potentials in Brain-Computer Interfacing

Hill, NJ.

Invited lecture on the bachelor & masters course "Introduction to Brain-Computer Interfacing", October 2009 (talk)

Abstract
An introduction to event-related potentials with specific reference to their use in brain-computer interfacing applications and research.

ei

PDF [BibTex]

PDF [BibTex]


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BCI2000 and Python

Hill, NJ.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract
A tutorial, with exercises, on how to integrate your own Python code with the BCI2000 software package.

ei

PDF [BibTex]

PDF [BibTex]


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Implementing a Signal Processing Filter in BCI2000 Using C++

Hill, NJ., Mellinger, J.

Invited lecture at the 5th International BCI2000 Workshop, October 2009 (talk)

Abstract
This tutorial shows how the functionality of the BCI2000 software package can be extended with one‘s own code, using BCI2000‘s C++ API.

ei

PDF [BibTex]

PDF [BibTex]


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Kernel Learning Approaches for Image Classification

Gehler, PV.

Biologische Kybernetik, Universität des Saarlandes, Saarbrücken, Germany, October 2009 (phdthesis)

Abstract
This thesis extends the use of kernel learning techniques to specific problems of image classification. Kernel learning is a paradigm in the field of machine learning that generalizes the use of inner products to compute similarities between arbitrary objects. In image classification one aims to separate images based on their visual content. We address two important problems that arise in this context: learning with weak label information and combination of heterogeneous data sources. The contributions we report on are not unique to image classification, and apply to a more general class of problems. We study the problem of learning with label ambiguity in the multiple instance learning framework. We discuss several different image classification scenarios that arise in this context and argue that the standard multiple instance learning requires a more detailed disambiguation. Finally we review kernel learning approaches proposed for this problem and derive a more efficient algorithm to solve them. The multiple kernel learning framework is an approach to automatically select kernel parameters. We extend it to its infinite limit and present an algorithm to solve the resulting problem. This result is then applied in two directions. We show how to learn kernels that adapt to the special structure of images. Finally we compare different ways of combining image features for object classification and present significant improvements compared to previous methods.

ei

PDF [BibTex]

PDF [BibTex]


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Learning Motor Primitives for Robotics

Kober, J., Peters, J., Oztop, E.

Advanced Telecommunications Research Center ATR, June 2009 (talk)

Abstract
The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.

ei

[BibTex]

[BibTex]


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Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Lampert, C.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009 (talk)

ei

Web [BibTex]

Web [BibTex]


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Kernel Methods in Computer Vision:Object Localization, Clustering,and Taxonomy Discovery

Blaschko, MB.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, March 2009 (phdthesis)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Motor Control and Learning in Table Tennis

Mülling, K.

Eberhard Karls Universität Tübingen, Gerrmany, 2009 (diplomathesis)

ei

[BibTex]

[BibTex]


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Hierarchical Clustering and Density Estimation Based on k-nearest-neighbor graphs

Drewe, P.

Eberhard Karls Universität Tübingen, Germany, 2009 (diplomathesis)

ei

[BibTex]

[BibTex]


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Learning with Structured Data: Applications to Computer Vision

Nowozin, S.

Technische Universität Berlin, Germany, 2009 (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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From Differential Equations to Differential Geometry: Aspects of Regularisation in Machine Learning

Steinke, F.

Universität des Saarlandes, Saarbrücken, Germany, 2009 (phdthesis)

ei

PDF [BibTex]


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Magnetische L10-FePt Nanostrukturen für höchste Datenspeicherdichten

Breitling, A.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

[BibTex]


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Ab-initio Elliott-Yafet modeling of ultrafast demagnetization after laser irradiation

Illg, C.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Element specific investigation of the magnetization profile at the CrO2/RuO2 interface

Zafar, K.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Bayesian Methods for Autonomous Learning Systems (Phd Thesis)

Ting, J.

Department of Computer Science, University of Southern California, Los Angeles, CA, 2009, clmc (phdthesis)

am

PDF [BibTex]

PDF [BibTex]


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Magnetic resonant reflectometry on exchange bias systems

Brück, S.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

link (url) [BibTex]

link (url) [BibTex]


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In-situ - Untersuchungen zu Interdiffusion und Magnetismus in magnetischen Multilayern

Schmidt, M.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]


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Theorie der elektronischen Zustände in oxidischen magnetischen Materialien

Kostoglou, C.

Universität Stuttgart, Stuttgart, 2009 (phdthesis)

mms

[BibTex]

[BibTex]


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Magnetooptische Untersuchungen an Ferromagnet- und Supraleiter-Nanosystemen und deren Hybriden

Treiber, S.

Universität Stuttgart, Stuttgart, 2009 (mastersthesis)

mms

[BibTex]

[BibTex]

2007


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Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism

Saigo, H., Hattori, M., Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83% accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.

ei

Web [BibTex]

2007


Web [BibTex]


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Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

ei

Web [BibTex]

Web [BibTex]


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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

ei

Web [BibTex]

Web [BibTex]


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An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM)method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets, and show that we perform better than the current state of the art. Furthermore, our method provides some insights as to which features are most useful for determining subcellular localization, which are in agreement with biological reasoning.

ei

Web [BibTex]

Web [BibTex]


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Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration

Hill, NJ.

Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

Abstract
I‘ll present a perspective on Brain-Computer Interface development from T{\"u}bingen. Some of the benefits promised by BCI technology lie in the near foreseeable future, and some further away. Our motivation is to make BCI technology feasible for the people who could benefit from what it has to offer soon: namely, people in the "completely locked-in" state. I‘ll mention some of the challenges of working with this user group, and explain the specific directions they have motivated us to take in developing experimental methods, algorithms, and software.

ei

[BibTex]

[BibTex]


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Some Theoretical Aspects of Human Categorization Behavior: Similarity and Generalization

Jäkel, F.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007, passed with "ausgezeichnet", summa cum laude, published online (phdthesis)

ei

PDF [BibTex]

PDF [BibTex]


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Statistical Learning Theory Approaches to Clustering

Jegelka, S.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007 (diplomathesis)

ei

PDF [BibTex]

PDF [BibTex]


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Policy Learning for Robotics

Peters, J.

14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

ei

Web [BibTex]

Web [BibTex]


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Hilbert Space Representations of Probability Distributions

Gretton, A.

2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

Abstract
Many problems in unsupervised learning require the analysis of features of probability distributions. At the most fundamental level, we might wish to determine whether two distributions are the same, based on samples from each - this is known as the two-sample or homogeneity problem. We use kernel methods to address this problem, by mapping probability distributions to elements in a reproducing kernel Hilbert space (RKHS). Given a sufficiently rich RKHS, these representations are unique: thus comparing feature space representations allows us to compare distributions without ambiguity. Applications include testing whether cancer subtypes are distinguishable on the basis of DNA microarray data, and whether low frequency oscillations measured at an electrode in the cortex have a different distribution during a neural spike. A more difficult problem is to discover whether two random variables drawn from a joint distribution are independent. It turns out that any dependence between pairs of random variables can be encoded in a cross-covariance operator between appropriate RKHS representations of the variables, and we may test independence by looking at a norm of the operator. We demonstrate this independence test by establishing dependence between an English text and its French translation, as opposed to French text on the same topic but otherwise unrelated. Finally, we show that this operator norm is itself a difference in feature means.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Regression with Intervals

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

ei

Web [BibTex]

Web [BibTex]


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MR-Based PET Attenuation Correction: Method and Validation

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B.

Joint Molecular Imaging Conference, September 2007 (talk)

Abstract
PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Bayesian methods for NMR structure determination

Habeck, M.

29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

ei

Web [BibTex]

Web [BibTex]


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Thinking Out Loud: Research and Development of Brain Computer Interfaces

Hill, NJ.

Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

Abstract
My principal interest is in applying machine-learning methods to the development of Brain-Computer Interfaces (BCI). This involves the classification of a user‘s intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced Amyotrophic Lateral Sclerosis) could use to communicate. Such systems have the potential to improve the lives of many people who would be otherwise completely unable to communicate, but they are still very much in the research and development stages.

ei

PDF [BibTex]

PDF [BibTex]