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2019


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A Kernel Stein Test for Comparing Latent Variable Models

Kanagawa, H., Jitkrittum, W., Mackey, L., Fukumizu, K., Gretton, A.

2019 (conference) Submitted

ei

arXiv [BibTex]

2019


arXiv [BibTex]


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Bistability of magnetic states in Fe-Pd nanocap arrays

Aravind, P. B., Heigl, M., Fix, M., Groß, F., Gräfe, J., Mary, A., Rajgowrav, C. R., Krupiński, M., Marszałek, M., Thomas, S., Anantharaman, M. R., Albrecht, M.

Nanotechnology, 30, pages: 405705, 2019 (article)

Abstract
Magnetic bistability between vortex and single domain states in nanostructures are of great interest from both fundamental and technological perspectives. In soft magnetic nanostructures, the transition from a uniform collinear magnetic state to a vortex state (or vice versa) induced by a magnetic field involves an energy barrier. If the thermal energy is large enough for overcoming this energy barrier, magnetic bistability with a hysteresis-free switching occurs between the two magnetic states. In this work, we tune this energy barrier by tailoring the composition of FePd alloys, which were deposited onto self-assembled particle arrays forming magnetic vortex structures on top of the particles. The bifurcation temperature, where a hysteresis-free transition occurs, was extracted from the temperature dependence of the annihilation and nucleation field which increases almost linearly with Fe content of the magnetic alloy. This study provides insights into the magnetization reversal process associated with magnetic bistability, which allows adjusting the bifurcation temperature range by the material properties of the nanosystem.

mms

link (url) [BibTex]

link (url) [BibTex]


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Phenomenal Causality and Sensory Realism

Bruijns, S. A., Meding, K., Schölkopf, B., Wichmann, F. A.

European Conference on Visual Perception (ECVP), 2019 (poster)

ei

[BibTex]

[BibTex]


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Learning to Disentangle Latent Physical Factors for Video Prediction

Zhu, D., Munderloh, M., Rosenhahn, B., Stückler, J.

In German Conference on Pattern Recognition (GCPR), 2019, to appear (inproceedings)

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dataset & evaluation code video preprint [BibTex]

dataset & evaluation code video preprint [BibTex]


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An international laboratory comparison study of volumetric and gravimetric hydrogen adsorption measurements

Hurst, K. E., Gennett, T., Adams, J., Allendorf, M. D., Balderas-Xicohténcatl, R., Bielewski, M., Edwards, B., Espinal, L., Fultz, B., Hirscher, M., Hudson, M. S. L., Hulvey, Z., Latroche, M., Liu, D., Kapelewski, M., Napolitano, E., Perry, Z. T., Purewal, J., Stavila, V., Veenstra, M., White, J. L., Yuan, Y., Zhou, H., Zlotea, C., Parilla, P.

{ChemPhysChem}, 20(15):1997-2009, Wiley-VCH, Weinheim, Germany, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Hydrogen Energy

Hirscher, M., Autrey, T., Orimo, S.

{ChemPhysChem}, 20, pages: 1153-1411, Wiley-VCH, Weinheim, Germany, 2019 (misc)

mms

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Ghosh, P., Losalka, A., Black, M. J.

In Proc. AAAI, 2019 (inproceedings)

Abstract
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, ``adversarial samples" and ``fooling samples", have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.

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link (url) Project Page [BibTex]


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Electromechanical actuation of dielectric liquid crystal elastomers for soft robotics

Davidson, Z., Shahsavan, H., Guo, Y., Hines, L., Xia, Y., Yang, S., Sitti, M.

Bulletin of the American Physical Society, APS, 2019 (article)

pi

[BibTex]

[BibTex]


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Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

Roos, F. D., Hennig, P.

2019 (conference) Accepted

Abstract
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.

pn

link (url) [BibTex]


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Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

Wenk, P., Gotovos, A., Bauer, S., Gorbach, N., Krause, A., Buhmann, J. M.

22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 (conference) Accepted

ei

PDF [BibTex]

PDF [BibTex]


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Occupancy Networks: Learning 3D Reconstruction in Function Space

Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2019, 2019 (inproceedings)

Abstract
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learning-based 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose Occupancy Networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.

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Code Video pdf suppmat Project Page [BibTex]

Code Video pdf suppmat Project Page [BibTex]


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From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

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arXiv [BibTex]


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A rational reinterpretation of dual process theories

Milli, S., Lieder, F., Griffiths, T.

2019 (article)

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DOI [BibTex]

DOI [BibTex]


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Probabilistic Linear Solvers: A Unifying View

Bartels, S., Cockayne, J., Ipsen, I. C. F., Hennig, P.

Statistics and Computing, 2019 (article) Accepted

pn

link (url) [BibTex]

link (url) [BibTex]


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3D Birds-Eye-View Instance Segmentation

Elich, C., Engelmann, F., Kontogianni, T., Leibe, B.

In German Conference on Pattern Recognition (GCPR), 2019, arXiv:1904.02199, to appear (inproceedings)

ev

[BibTex]

[BibTex]


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Fisher Efficient Inference of Intractable Models

Liu, S., Kanamori, T., Jitkrittum, W., Chen, Y.

Advances in Neural Information Processing Systems 32, 33rd Annual Conference on Neural Information Processing Systems, 2019 (conference) To be published

ei

arXiv [BibTex]

arXiv [BibTex]


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The route to supercurrent transparent ferromagnetic barriers in superconducting matrix

Ivanov, Y. P., Soltan, S., Albrecht, J., Goering, E., Schütz, G., Zhang, Z., Chuvilin, A.

{ACS Nano}, 13(5):5655-5661, American Chemical Society, Washington, DC, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Systematic experimental study on quantum sieving of hydrogen isotopes in metal-amide-imidazolate frameworks with narrow 1-D channels

Mondal, S. S., Kreuzer, A., Behrens, K., Schütz, G., Holdt, H., Hirscher, M.

{ChemPhysChem}, 20(10):1311-1315, Wiley-VCH, Weinheim, Germany, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Artifacts from manganese reduction in rock samples prepared by focused ion beam (FIB) slicing for X-ray microspectroscopy

Macholdt, D. S., Förster, J., Müller, M., Weber, B., Kappl, M., Kilcoyne, A. L. D., Weigand, M., Leitner, J., Jochum, K. P., Pöhlker, C., Andreae, M. O.

{Geoscientific instrumentation, methods and data systems}, 8(1):97-111, Copernicus Publ., Göttingen, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Mixed-state magnetotransport properties of MgB2 thin film prepared by pulsed laser deposition on an Al2O3 substrate

Alzayed, N. S., Shahabuddin, M., Ramey, S. M., Soltan, S.

{Journal of Materials Science: Materials in Electronics}, 30(2):1547-1552, Springer, Norwell, MA, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Comparison of theories of fast and ultrafast magnetization dynamics

Fähnle, M.

{Journal of Magnetism and Magnetic Materials}, 469, pages: 28-29, NH, Elsevier, Amsterdam, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Concepts for improving hydrogen storage in nanoporous materials

Broom, D. P., Webb, C. J., Fanourgakis, G. S., Froudakis, G. E., Trikalitis, P. N., Hirscher, M.

{International Journal of Hydrogen Energy}, 44(15):7768-7779, Elsevier, Amsterdam, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Controlling dislocation nucleation-mediatd plasticity in nanostructures via surface modification

Shin, J., Chen, L. Y., Sanli, U. T., Richter, G., Labat, S., Richard, M., Cornelius, T., Thomas, O., Gianola, D. S.

{Acta Materialia}, 166, pages: 572-586, Elsevier Science, Kidlington, 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]


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Reprogrammability and scalability of magnonic Fibonacci quasicrystals

Lisiecki, F., Rychly, J., Kuswik, P., Glowinski, H., Klos, J. W., Groß, F., Bykova, I., Weigand, M., Zelent, M., Goering, E. J., Schütz, G., Gubbiotti, G., Krawczyk, M., Stobiecki, F., Dubowik, J., Gräfe, J.

{Physical Review Applied}, 11(5), American Physical Society, College Park, Md. [u.a.], 2019 (article)

mms

DOI [BibTex]

DOI [BibTex]

2004


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Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface

Hill, J., Lal, T., Bierig, K., Birbaumer, N., Schölkopf, B.

In BioCAS04, (S3/5/INV- S3/17-20):4, IEEE Computer Society, Los Alamitos, CA, USA, 2004 IEEE International Workshop on Biomedical Circuits and Systems, December 2004 (inproceedings)

Abstract
Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user‘s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.

ei

PDF Web DOI [BibTex]

2004


PDF Web DOI [BibTex]


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On the representation, learning and transfer of spatio-temporal movement characteristics

Ilg, W., Bakir, GH., Mezger, J., Giese, M.

International Journal of Humanoid Robotics, 1(4):613-636, December 2004 (article)

ei

[BibTex]

[BibTex]


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Insect-inspired estimation of egomotion

Franz, MO., Chahl, JS., Krapp, HG.

Neural Computation, 16(11):2245-2260, November 2004 (article)

Abstract
Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the construction of an estimator consisting of a linear combination of optic flow vectors that incorporates prior knowledge both about the distance distribution of the environment, and about the noise and egomotion statistics of the sensor. The estimator is tested on a gantry carrying an omnidirectional vision sensor. The experiments show that the proposed approach leads to accurate and robust estimates of rotation rates, whereas translation estimates are of reasonable quality, albeit less reliable.

ei

PDF PostScript Web DOI [BibTex]

PDF PostScript Web DOI [BibTex]


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Efficient face detection by a cascaded support-vector machine expansion

Romdhani, S., Torr, P., Schölkopf, B., Blake, A.

Proceedings of The Royal Society of London A, 460(2501):3283-3297, A, November 2004 (article)

Abstract
We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Joint Kernel Maps

Weston, J., Schölkopf, B., Bousquet, O., Mann, .., Noble, W.

(131), Max-Planck-Institute for Biological Cybernetics, Tübingen, November 2004 (techreport)

ei

PDF [BibTex]

PDF [BibTex]


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Discrete vs. Continuous: Two Sides of Machine Learning

Zhou, D.

October 2004 (talk)

Abstract
We consider the problem of transductive inference. In many real-world problems, unlabeled data is far easier to obtain than labeled data. Hence transductive inference is very significant in many practical problems. According to Vapnik's point of view, one should predict the function value only on the given points directly rather than a function defined on the whole space, the latter being a more complicated problem. Inspired by this idea, we develop discrete calculus on finite discrete spaces, and then build discrete regularization. A family of transductive algorithms is naturally derived from this regularization framework. We validate the algorithms on both synthetic and real-world data from text/web categorization to bioinformatics problems. A significant by-product of this work is a powerful way of ranking data based on examples including images, documents, proteins and many other kinds of data. This talk is mainly based on the followiing contribution: (1) D. Zhou and B. Sch{\"o}lkopf: Transductive Inference with Graphs, MPI Technical report, August, 2004; (2) D. Zhou, B. Sch{\"o}lkopf and T. Hofmann. Semi-supervised Learning on Directed Graphs. NIPS 2004; (3) D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Sch{\"o}lkopf. Learning with Local and Global Consistency. NIPS 2003.

ei

PDF [BibTex]


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S-cones contribute to flicker brightness in human vision

Wehrhahn, C., Hill, NJ., Dillenburger, B.

34(174.12), 34th Annual Meeting of the Society for Neuroscience (Neuroscience), October 2004 (poster)

Abstract
In the retina of primates three cone types sensitive to short, middle and long wavelengths of light convert photons into electrical signals. Many investigators have presented evidence that, in color normal observers, the signals of cones sensitive to short wavelengths of light (S-cones) do not contribute to the perception of brightness of a colored surface when this is alternated with an achromatic reference (flicker brightness). Other studies indicate that humans do use S-cone signals when performing this task. Common to all these studies is the small number of observers, whose performance data are reported. Considerable variability in the occurrence of cone types across observers has been found, but, to our knowledge, no cone counts exist from larger populations of humans. We reinvestigated how much the S-cones contribute to flicker brightness. 76 color normal observers were tested in a simple psychophysical procedure neutral to the cone type occurence (Teufel & Wehrhahn (2000), JOSA A 17: 994 - 1006). The data show that, in the majority of our observers, S-cones provide input with a negative sign - relative to L- and M-cone contribution - in the task in question. There is indeed considerable between-subject variability such that for 20 out of 76 observers the magnitude of this input does not differ significantly from 0. Finally, we argue that the sign of S-cone contribution to flicker brightness perception by an observer cannot be used to infer the relative sign their contributions to the neuronal signals carrying the information leading to the perception of flicker brightness. We conclude that studies which use only a small number of observers may easily fail to find significant evidence for the small but significant population tendency for the S-cones to contribute to flicker brightness. Our results confirm all earlier results and reconcile their contradictory interpretations.

ei

Web [BibTex]

Web [BibTex]


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Modelling Spikes with Mixtures of Factor Analysers

Görür, D., Rasmussen, C., Tolias, A., Sinz, F., Logothetis, N.

In Pattern Recognition, pages: 391-398, LNCS 3175, (Editors: Rasmussen, C. E. , H.H. Bülthoff, B. Schölkopf, M.A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, September 2004 (inproceedings)

Abstract
Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging problem. We consider the spike sorting problem using a generative model,mixtures of factor analysers, which concurrently performs clustering and feature extraction. The most important advantage of this method is that it quantifies the certainty with which the spikes are classified. This can be used as a means for evaluating the quality of clustering and therefore spike isolation. Using this method, nearly simultaneously occurring spikes can also be modelled which is a hard task for many of the spike sorting methods. Furthermore, modelling the data with a generative model allows us to generate simulated data.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Learning Depth From Stereo

Sinz, F., Candela, J., BakIr, G., Rasmussen, C., Franz, M.

In 26th DAGM Symposium, pages: 245-252, LNCS 3175, (Editors: Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, September 2004 (inproceedings)

Abstract
We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1.~The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2.~A generic machine learning approach where the mapping from image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic learning approach, in addition to simplifying the procedure of calibration, can lead to higher depth accuracies than classical calibration although no specific domain knowledge is used.

ei

PDF PostScript Web [BibTex]

PDF PostScript Web [BibTex]


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Grundlagen von Support Vector Maschinen und Anwendungen in der Bildverarbeitung

Eichhorn, J.

September 2004 (talk)

Abstract
Invited talk at the workshop "Numerical, Statistical and Discrete Methods in Image Processing" at the TU M{\"u}nchen (in GERMAN)

ei

PDF [BibTex]


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Advanced Lectures on Machine Learning

Bousquet, O., von Luxburg, U., Rätsch, G.

ML Summer Schools 2003, LNAI 3176, pages: 240, Springer, Berlin, Germany, ML Summer Schools, September 2004 (proceedings)

Abstract
Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in T{\"u}bingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

ei

Web [BibTex]

Web [BibTex]


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Pattern Recognition: 26th DAGM Symposium, LNCS, Vol. 3175

Rasmussen, C., Bülthoff, H., Giese, M., Schölkopf, B.

Proceedings of the 26th Pattern Recognition Symposium (DAGM‘04), pages: 581, Springer, Berlin, Germany, 26th Pattern Recognition Symposium, August 2004 (proceedings)

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Semi-Supervised Induction

Yu, K., Tresp, V., Zhou, D.

(141), Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, August 2004 (techreport)

Abstract
Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Kernel Methods in Computational Biology

Schölkopf, B., Tsuda, K., Vert, J.

pages: 410, Computational Molecular Biology, MIT Press, Cambridge, MA, USA, August 2004 (book)

Abstract
Modern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology. Following three introductory chapters—an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology—the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods.

ei

Web [BibTex]

Web [BibTex]


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Learning kernels from biological networks by maximizing entropy

Tsuda, K., Noble, W.

Bioinformatics, 20(Suppl. 1):i326-i333, August 2004 (article)

Abstract
Motivation: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. Results: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein–protein interaction networks.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Learning to Find Graph Pre-Images

BakIr, G., Zien, A., Tsuda, K.

In Pattern Recognition, pages: 253-261, (Editors: Rasmussen, C. E., H. H. Bülthoff, B. Schölkopf, M. A. Giese), Springer, Berlin, Germany, 26th DAGM Symposium, August 2004 (inproceedings)

Abstract
The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem.

ei

PostScript PDF DOI [BibTex]

PostScript PDF DOI [BibTex]


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Masking effect produced by Mach bands on the detection of narrow bars of random polarity

Henning, GB., Hoddinott, KT., Wilson-Smith, ZJ., Hill, NJ.

Journal of the Optical Society of America, 21(8):1379-1387, A, August 2004 (article)

ei

[BibTex]

[BibTex]


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Exponential Families for Conditional Random Fields

Altun, Y., Smola, A., Hofmann, T.

In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2004), pages: 2-9, (Editors: Chickering, D.M. , J.Y. Halpern), Morgan Kaufmann, San Francisco, CA, USA, 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI), July 2004 (inproceedings)

Abstract
In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Object categorization with SVM: kernels for local features

Eichhorn, J., Chapelle, O.

(137), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

ei

PDF [BibTex]

PDF [BibTex]


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Hilbertian Metrics and Positive Definite Kernels on Probability Measures

Hein, M., Bousquet, O.

(126), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, July 2004 (techreport)

Abstract
We investigate the problem of defining Hilbertian metrics resp. positive definite kernels on probability measures, continuing previous work. This type of kernels has shown very good results in text classification and has a wide range of possible applications. In this paper we extend the two-parameter family of Hilbertian metrics of Topsoe such that it now includes all commonly used Hilbertian metrics on probability measures. This allows us to do model selection among these metrics in an elegant and unified way. Second we investigate further our approach to incorporate similarity information of the probability space into the kernel. The analysis provides a better understanding of these kernels and gives in some cases a more efficient way to compute them. Finally we compare all proposed kernels in two text and one image classification problem.

ei

PDF [BibTex]

PDF [BibTex]