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2018


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Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

ei

DOI Project Page [BibTex]

2018


DOI Project Page [BibTex]


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Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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A Differentially Private Kernel Two-Sample Test

Raj*, A., Law*, L., Sejdinovic*, D., Park, M.

2018, *equal contribution (conference) Submitted

ei

[BibTex]

[BibTex]


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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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A Causal Perspective on Deep Representation Learning

Suter, R.

ETH Zurich, 2018 (mastersthesis)

ei

[BibTex]


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Quantum machine learning: a classical perspective

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L.

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209):20170551, 2018 (article)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

ei

link (url) [BibTex]

link (url) [BibTex]


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MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 15(6):066011, 2018 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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f-Divergence constrained policy improvement

Belousov, B., Peters, J.

Journal of Machine Learning Research, 2018 (article) Submitted

ei

Project Page [BibTex]

Project Page [BibTex]


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Phylogenetic convolutional neural networks in metagenomics

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C.

BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

ei

DOI [BibTex]

DOI [BibTex]


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Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A.

International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

ei

[BibTex]

[BibTex]


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Linking imaging to omics utilizing image-guided tissue extraction

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J.

Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

ei

[BibTex]

[BibTex]


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Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Photorealistic Video Super Resolution

Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B.

Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), 2018 (poster)

ei

[BibTex]

[BibTex]


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Denotational Validation of Higher-order Bayesian Inference

Ścibior, A., Kammar, O., Vákár, M., Staton, S., Yang, H., Cai, Y., Ostermann, K., Moss, S. K., Heunen, C., Ghahramani, Z.

Proceedings of the ACM on Principles of Programming Languages (POPL), 2(Article No. 60):1-29, ACM, 2018 (conference)

ei

DOI Project Page [BibTex]

DOI Project Page [BibTex]


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Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M.

Neuron, 100(5):1224-1240, 2018 (article)

ei

link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


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Retinal image quality of the human eye across the visual field

Meding, K., Hirsch, M., Wichmann, F. A.

14th Biannual Conference of the German Society for Cognitive Science (KOGWIS 2018), 2018 (poster)

ei

[BibTex]

[BibTex]


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In-Hand Object Stabilization by Independent Finger Control

Veiga, F. F., Edin, B. B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

ei

Project Page [BibTex]

Project Page [BibTex]


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Visualizing and understanding Sum-Product Networks

Vergari, A., Di Mauro, N., Esposito, F.

Machine Learning, 2018 (article)

ei

DOI [BibTex]

DOI [BibTex]


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Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

ei

Project Page [BibTex]

Project Page [BibTex]


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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei pn

[BibTex]

[BibTex]


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A machine learning approach to taking EEG-based computer interfaces out of the lab

Jayaram, V.

Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

ei

[BibTex]

[BibTex]


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Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Krüger, M, Braun, DA

Entropy, 20(1:1):1-28, January 2018 (article)

Abstract
Living organisms from single cells to humans need to adapt continuously to respond to changes in their environment. The process of behavioural adaptation can be thought of as improving decision-making performance according to some utility function. Here, we consider an abstract model of organisms as decision-makers with limited information-processing resources that trade off between maximization of utility and computational costs measured by a relative entropy, in a similar fashion to thermodynamic systems undergoing isothermal transformations. Such systems minimize the free energy to reach equilibrium states that balance internal energy and entropic cost. When there is a fast change in the environment, these systems evolve in a non-equilibrium fashion because they are unable to follow the path of equilibrium distributions. Here, we apply concepts from non-equilibrium thermodynamics to characterize decision-makers that adapt to changing environments under the assumption that the temporal evolution of the utility function is externally driven and does not depend on the decision-maker’s action. This allows one to quantify performance loss due to imperfect adaptation in a general manner and, additionally, to find relations for decision-making similar to Crooks’ fluctuation theorem and Jarzynski’s equality. We provide simulations of several exemplary decision and inference problems in the discrete and continuous domains to illustrate the new relations.

ei

DOI [BibTex]

DOI [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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A Tutorial on Spectral Clustering

von Luxburg, U.

Statistics and Computing, 17(4):395-416, December 2007 (article)

Abstract
In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [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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A Tutorial on Kernel Methods for Categorization

Jäkel, F., Schölkopf, B., Wichmann, F.

Journal of Mathematical Psychology, 51(6):343-358, December 2007 (article)

Abstract
The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of catego rization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key concepts—the so-called kernel trick, the representer theorem and regularization—which may open up the possibility that insights from machine learning can feed back into psychology.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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Accurate Splice site Prediction Using Support Vector Machines

Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.

BMC Bioinformatics, 8(Supplement 10):1-16, December 2007 (article)

Abstract
Background: For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks. Results: In this work we consider Support Vector Machines for splice site recognition. We employ the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in several experiments where we compare its prediction accuracy with that of recently proposed systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our performance estimates indicate that splice sites can be recognized very accurately in these genomes and that our method outperforms many other methods including Markov Chains, GeneSplicer and SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction tool ready to be used for incorporation in a gene finder. Availability: Data, splits, additional information on the model selection, the whole genome predictions, as well as the stand-alone prediction tool are available for download at http:// www.fml.mpg.de/raetsch/projects/splice.

ei

PDF DOI [BibTex]

PDF DOI [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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Towards compliant humanoids: an experimental assessment of suitable task space position/orientation controllers

Nakanishi, J., Mistry, M., Peters, J., Schaal, S.

In IROS 2007, 2007, pages: 2520-2527, (Editors: Grant, E. , T. C. Henderson), IEEE Service Center, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems, November 2007 (inproceedings)

Abstract
Compliant control will be a prerequisite for humanoid robotics if these robots are supposed to work safely and robustly in human and/or dynamic environments. One view of compliant control is that a robot should control a minimal number of degrees-of-freedom (DOFs) directly, i.e., those relevant DOFs for the task, and keep the remaining DOFs maximally compliant, usually in the null space of the task. This view naturally leads to task space control. However, surprisingly few implementations of task space control can be found in actual humanoid robots. This paper makes a first step towards assessing the usefulness of task space controllers for humanoids by investigating which choices of controllers are available and what inherent control characteristics they have—this treatment will concern position and orientation control, where the latter is based on a quaternion formulation. Empirical evaluations on an anthropomorphic Sarcos master arm illustrate the robustness of the different controllers as well as the eas e of implementing and tuning them. Our extensive empirical results demonstrate that simpler task space controllers, e.g., classical resolved motion rate control or resolved acceleration control can be quite advantageous in face of inevitable modeling errors in model-based control, and that well chosen formulations are easy to implement and quite robust, such that they are useful for humanoids.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


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MR-Based PET Attenuation Correction: Method and Validation

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

2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M16-6):1-2, November 2007 (poster)

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 PDF [BibTex]

PDF PDF [BibTex]


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Estimating receptive fields without spike-triggering

Macke, J., Zeck, G., Bethge, M.

37th annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37(768.1):1, November 2007 (poster)

ei

Web [BibTex]

Web [BibTex]


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Evaluation of Deformable Registration Methods for MR-CT Atlas Alignment

Scheel, V., Hofmann, M., Rehfeld, N., Judenhofer, M., Claussen, C., Pichler, B.

2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M13-121):1, November 2007 (poster)

Abstract
Deformable registration methods are essential for multimodality imaging. Many different methods exist but due to the complexity of the deformed images a direct comparison of the methods is difficult. One particular application that requires high accuracy registration of MR-CT images is atlas-based attenuation correction for PET/MR. We compare four deformable registration algorithms for 3D image data included in the Open Source "National Library of Medicine Insight Segmentation and Registration Toolkit" (ITK). An interactive landmark based registration using MiraView (Siemens) has been used as gold standard. The automatic algorithms provided by ITK are based on the metrics Mattes mutual information as well as on normalized mutual information. The transformations are calculated by interpolating over a uniform B-Spline grid laying over the image to be warped. The algorithms were tested on head images from 10 subjects. We implemented a measure which segments head interior bone and air based on the CT images and l ow intensity classes of corresponding MRI images. The segmentation of bone is performed by individually calculating the lowest Hounsfield unit threshold for each CT image. The compromise is made by quantifying the number of overlapping voxels of the remaining structures. We show that the algorithms provided by ITK achieve similar or better accuracy than the time-consuming interactive landmark based registration. Thus, ITK provides an ideal platform to generate accurately fused datasets from different modalities, required for example for building training datasets for Atlas-based attenuation correction.

ei

PDF [BibTex]

PDF [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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Performance Stabilization and Improvement in Graph-based Semi-supervised Learning with Ensemble Method and Graph Sharpening

Choi, I., Shin, H.

In Korean Data Mining Society Conference, pages: 257-262, Korean Data Mining Society, Seoul, Korea, Korean Data Mining Society Conference, November 2007 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


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A time/frequency decomposition of information transmission by LFPs and spikes in the primary visual cortex

Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M., Logothetis, N., Panzeri, S.

37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37, pages: 1, November 2007 (poster)

ei

Web [BibTex]

Web [BibTex]


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Mining expression-dependent modules in the human interaction network

Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K.

BMC Bioinformatics, 8(Suppl. 8):S4, November 2007 (poster)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]