Header logo is ei


2018


no image
Representation of sensory uncertainty in macaque visual cortex

Goris, R., Henaff, O., Meding, K.

Computational and Systems Neuroscience (COSYNE) 2018, March 2018 (poster)

[BibTex]

2018

[BibTex]


no image
Generalized phase locking analysis of electrophysiology data

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M.

7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)

link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


no image
Maschinelles Lernen: Entwicklung ohne Grenzen?

Schökopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

[BibTex]


no image
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)

[BibTex]

[BibTex]


no image
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)

[BibTex]

[BibTex]


no image
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)

[BibTex]

[BibTex]


no image
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)

Project Page [BibTex]

Project Page [BibTex]

2014


no image
Method and device for blind correction of optical aberrations in a digital image

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.

International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)

[BibTex]

2014


no image
Dynamical source analysis of hippocampal sharp-wave ripple episodes

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

Bernstein Conference, 2014 (poster)

DOI [BibTex]

DOI [BibTex]


no image
FID-guided retrospective motion correction based on autofocusing

Babayeva, M., Loktyushin, A., Kober, T., Granziera, C., Nickisch, H., Gruetter, R., Krueger, G.

Joint Annual Meeting ISMRM-ESMRMB, Milano, Italy, 2014 (poster)

[BibTex]

[BibTex]


no image
Single-Source Domain Adaptation with Target and Conditional Shift

Zhang, K., Schölkopf, B., Muandet, K., Wang, Z., Zhou, Z., Persello, C.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

[BibTex]

[BibTex]


no image
Higher-Order Tensors in Diffusion Imaging

Schultz, T., Fuster, A., Ghosh, A., Deriche, R., Florack, L., Lim, L.

In Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data, pages: 129-161, Mathematics + Visualization, (Editors: Westin, C.-F., Vilanova, A. and Burgeth, B.), Springer, 2014 (inbook)

[BibTex]

[BibTex]


no image
Fuzzy Fibers: Uncertainty in dMRI Tractography

Schultz, T., Vilanova, A., Brecheisen, R., Kindlmann, G.

In Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, pages: 79-92, 8, Mathematics + Visualization, (Editors: Hansen, C. D., Chen, M., Johnson, C. R., Kaufman, A. E. and Hagen, H.), Springer, 2014 (inbook)

[BibTex]

[BibTex]


no image
Cluster analysis of sharp-wave ripple field potential signatures in the macaque hippocampus

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

Computational and Systems Neuroscience Meeting (COSYNE), 2014 (poster)

[BibTex]

[BibTex]


no image
Nonconvex Proximal Splitting with Computational Errors

Sra, S.

In Regularization, Optimization, Kernels, and Support Vector Machines, pages: 83-102, 4, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), CRC Press, 2014 (inbook)

[BibTex]

[BibTex]


no image
Active Learning - Modern Learning Theory

Balcan, M., Urner, R.

In Encyclopedia of Algorithms, (Editors: Kao, M.-Y.), Springer Berlin Heidelberg, 2014 (incollection)

link (url) DOI [BibTex]

link (url) DOI [BibTex]


no image
oxel level [18]F-FDG PET/MRI unsupervised segmentation of the tumor microenvironment

Katiyar, P., Divine, M. R., Pichler, B. J., Disselhorst, J. A.

World Molecular Imaging Conference, 2014 (poster)

[BibTex]

[BibTex]

2000


no image
Robust ensemble learning

Rätsch, G., Schölkopf, B., Smola, A., Mika, S., Onoda, T., Müller, K.

In Advances in Large Margin Classifiers, pages: 207-220, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D. Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

2000

[BibTex]


no image
Entropy numbers for convex combinations and MLPs

Smola, A., Elisseeff, A., Schölkopf, B., Williamson, R.

In Advances in Large Margin Classifiers, pages: 369-387, Neural Information Processing Series, (Editors: AJ Smola and PL Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA,, October 2000 (inbook)

[BibTex]

[BibTex]


no image
Natural Regularization from Generative Models

Oliver, N., Schölkopf, B., Smola, A.

In Advances in Large Margin Classifiers, pages: 51-60, Neural Information Processing Series, (Editors: AJ Smola and PJ Bartlett and B Schölkopf and D Schuurmans), MIT Press, Cambridge, MA, USA, October 2000 (inbook)

[BibTex]

[BibTex]


no image
Solving Satisfiability Problems with Genetic Algorithms

Harmeling, S.

In Genetic Algorithms and Genetic Programming at Stanford 2000, pages: 206-213, (Editors: Koza, J. R.), Stanford Bookstore, Stanford, CA, USA, June 2000 (inbook)

Abstract
We show how to solve hard 3-SAT problems using genetic algorithms. Furthermore, we explore other genetic operators that may be useful to tackle 3-SAT problems, and discuss their pros and cons.

PDF [BibTex]

PDF [BibTex]


no image
Contrast discrimination using periodic pulse trains

Wichmann, F., Henning, G.

pages: 74, 3. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2000 (poster)

Abstract
Understanding contrast transduction is essential for understanding spatial vision. Previous research (Wichmann et al. 1998; Wichmann, 1999; Henning and Wichmann, 1999) has demonstrated the importance of high contrasts to distinguish between alternative models of contrast discrimination. However, the modulation transfer function of the eye imposes large contrast losses on stimuli, particularly for stimuli of high spatial frequency, making high retinal contrasts difficult to obtain using sinusoidal gratings. Standard 2AFC contrast discrimination experiments were conducted using periodic pulse trains as stimuli. Given our Mitsubishi display we achieve stimuli with up to 160% contrast at the fundamental frequency. The shape of the threshold versus (pedestal) contrast (TvC) curve using pulse trains shows the characteristic dipper shape, i.e. contrast discrimination is sometimes “easier” than detection. The rising part of the TvC function has the same slope as that measured for contrast discrimination using sinusoidal gratings of the same frequency as the fundamental. Periodic pulse trains offer the possibility to explore the visual system’s properties using high retinal contrasts. Thus they might prove useful in tasks other than contrast discrimination. Second, at least for high spatial frequencies (8 c/deg) it appears that contrast discrimination using sinusoids and periodic pulse trains results in virtually identical TvC functions, indicating a lack of probability summation. Further implications of these results are discussed.

Web [BibTex]

Web [BibTex]


no image
Subliminale Darbietung verkehrsrelevanter Information in Kraftfahrzeugen

Staedtgen, M., Hahn, S., Franz, MO., Spitzer, M.

pages: 98, (Editors: H.H. Bülthoff, K.R. Gegenfurtner, H.A. Mallot), 3. T{\"u}binger Wahrnehmungskonferenz (TWK), February 2000 (poster)

Abstract
Durch moderne Bildverarbeitungstechnologien ist es m{\"o}glich, in Kraftfahrzeugen bestimmte kritische Verkehrssituationen automatisch zu erkennen und den Fahrer zu warnen bzw. zu informieren. Ein Problem ist dabei die Darbietung der Ergebnisse, die den Fahrer m{\"o}glichst wenig belasten und seine Aufmerksamkeit nicht durch zus{\"a}tzliche Warnleuchten oder akustische Signale vom Verkehrsgeschehen ablenken soll. In einer Reihe von Experimenten wurde deshalb untersucht, ob subliminal dargebotene, das heißt nicht bewußt wahrgenommene, verkehrsrelevante Informationen verhaltenswirksam werden und zur Informations{\"u}bermittlung an den Fahrer genutzt werden k{\"o}nnen. In einem Experiment zur semantischen Bahnung konnte mit Hilfe einer lexikalischen Entscheidungsaufgabe gezeigt werden, daß auf den Straßenverkehr bezogene Worte schneller verarbeitet werden, wenn vorher ein damit in Zusammenhang stehendes Bild eines Verkehrsschildes subliminal pr{\"a}sentiert wurde. Auch bei parafovealer Darbietung der subliminalen Stimuli wurde eine Beschleunigung erzielt. In einer visuellen Suchaufgabe wurden in Bildern realer Verkehrssituationen Verkehrszeichen schneller entdeckt, wenn das Bild des Verkehrszeichens vorher subliminal dargeboten wurde. In beiden Experimenten betrug die Pr{\"a}sentationszeit f{\"u}r die Hinweisreize 17 ms, zus{\"a}tzlich wurde durch Vorw{\"a}rts- und R{\"u}ckw{\"a}rtsmaskierung die bewußteWahrnehmung verhindert. Diese Laboruntersuchungen zeigten, daß sich auch im Kontext des Straßenverkehrs Beschleunigungen der Informationsverarbeitung durch subliminal dargebotene Stimuli erreichen lassen. In einem dritten Experiment wurde die Darbietung eines subliminalen Hinweisreizes auf die Reaktionszeit beim Bremsen in einem realen Fahrversuch untersucht. Die Versuchspersonen (n=17) sollten so schnell wie m{\"o}glich bremsen, wenn die Bremsleuchten eines im Abstand von 12-15 m voran fahrenden Fahrzeuges aufleuchteten. In 50 von insgesamt 100 Durchg{\"a}ngen wurde ein subliminaler Stimulus (zwei rote Punkte mit einem Zentimeter Durchmesser und zehn Zentimeter Abstand) 150 ms vor Aufleuchten der Bremslichter pr{\"a}sentiert. Die Darbietung erfolgte durch ein im Auto an Stelle des Tachometers integriertes TFT-LCD Display. Im Vergleich zur Reaktion ohne subliminalen Stimulus verk{\"u}rzte sich die Reaktionszeit dadurch signifikant um 51 ms. In den beschriebenen Experimenten konnte gezeigt werden, daß die subliminale Darbietung verkehrsrelevanter Information auch in Kraftfahrzeugen verhaltenswirksam werden kann. In Zukunft k{\"o}nnte durch die Kombination der online-Bildverarbeitung im Kraftfahrzeug mit subliminaler Darbietung der Ergebnisse eine Erh{\"o}hung der Verkehrssicherheit und des Komforts erreicht werden.

Web [BibTex]

Web [BibTex]


no image
Statistical Learning and Kernel Methods

Schölkopf, B.

In CISM Courses and Lectures, International Centre for Mechanical Sciences Vol.431, CISM Courses and Lectures, International Centre for Mechanical Sciences, 431(23):3-24, (Editors: G Della Riccia and H-J Lenz and R Kruse), Springer, Vienna, Data Fusion and Perception, 2000 (inbook)

[BibTex]

[BibTex]


no image
An Introduction to Kernel-Based Learning Algorithms

Müller, K., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.

In Handbook of Neural Network Signal Processing, 4, (Editors: Yu Hen Hu and Jang-Neng Hwang), CRC Press, 2000 (inbook)

[BibTex]

[BibTex]

1995


no image
Image segmentation from motion: just the loss of high-spatial-frequency content ?

Wichmann, F., Henning, G.

Perception, 24, pages: S19, 1995 (poster)

Abstract
The human contrast sensitivity function (CSF) is bandpass for stimuli of low temporal frequency but, for moving stimuli, results in a low-pass CSF with large high spatial-frequency losses. Thus the high spatial-frequency content of images moving on the retina cannot be seen; motion perception could be facilitated by, or even be based on, the selective loss of high spatial-frequency content. 2-AFC image segmentation experiments were conducted with segmentation based on motion or on form. In the latter condition, the form difference mirrored that produced by moving stimuli. This was accomplished by generating stimulus elements which were spectrally either broadband or low-pass. For the motion used, the spectral difference between static broadband and static low-pass elements matched the spectral difference between moving and static broadband elements. On the hypothesis that segmentation from motion is based on the detection of regions devoid of high spatial-frequencies, both tasks should be similarly difficult for human observers. However, neither image segmentation (nor, incidentally, motion detection) was sensitive to the high spatial-frequency content of the stimuli. Thus changes in perceptual form produced by moving stimuli appear not to be used as a cue for image segmentation.

[BibTex]