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2002


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Contrast discrimination with pulse-trains in pink noise

Henning, G., Bird, C., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

Abstract
Detection performance was measured with sinusoidal and pulse-train gratings. Although the 2.09-c/deg pulse-train, or line gratings, contained at least 8 harmonics all at equal contrast, they were no more detectable than their most detectable component. The addition of broadband pink noise designed to equalize the detectability of the components of the pulse train made the pulse train about a factor of four more detectable than any of its components. However, in contrast-discrimination experiments, with a pedestal or masking grating of the same form and phase as the signal and 15% contrast, the noise did not affect the discrimination performance of the pulse train relative to that obtained with its sinusoidal components. We discuss the implications of these observations for models of early vision in particular the implications for possible sources of internal noise.

PDF [BibTex]

2002

PDF [BibTex]


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A kernel approach for learning from almost orthogonal patterns

Schölkopf, B., Weston, J., Eskin, E., Leslie, C., Noble, W.

In Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, 2430/2431, pages: 511-528, Lecture Notes in Computer Science, (Editors: T Elomaa and H Mannila and H Toivonen), Springer, Berlin, Germany, 13th European Conference on Machine Learning (ECML) and 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'2002), 2002 (inproceedings)

PostScript DOI [BibTex]

PostScript DOI [BibTex]


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Optimal linear estimation of self-motion - a real-world test of a model of fly tangential neurons

Franz, MO.

SAB 02 Workshop, Robotics as theoretical biology, 7th meeting of the International Society for Simulation of Adaptive Behaviour (SAB), (Editors: Prescott, T.; Webb, B.), 2002 (poster)

Abstract
The tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion (see example in Fig.1). We examine whether a simplified linear model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal linear estimator incorporating prior knowledge both about the distance distribution of the environment, and about the noise and self-motion statistics of the sensor. The optimal estimator is tested on a gantry carrying an omnidirectional vision sensor that can be moved along three translational and one rotational degree of freedom. The experiments indicate that the proposed approach yields accurate results for rotation estimates, independently of the current translation and scene layout. Translation estimates, however, turned out to be sensitive to simultaneous rotation and to the particular distance distribution of the scene. The gantry experiments confirm that the receptive field organization of the tangential neurons allows them, as an ensemble, to extract self-motion from the optic flow.

PDF [BibTex]

PDF [BibTex]


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Choosing Multiple Parameters for Support Vector Machines

Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.

Machine Learning, 46(1):131-159, 2002 (article)

Abstract
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVM) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Infinite Mixtures of Gaussian Process Experts

Rasmussen, CE., Ghahramani, Z.

In (Editors: Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin), 2002 (inproceedings)

Abstract
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Marginalized kernels for RNA sequence data analysis

Kin, T., Tsuda, K., Asai, K.

In Genome Informatics 2002, pages: 112-122, (Editors: Lathtop, R. H.; Nakai, K.; Miyano, S.; Takagi, T.; Kanehisa, M.), Genome Informatics, 2002, (Best Paper Award) (inproceedings)

Web [BibTex]

Web [BibTex]


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Luminance Artifacts on CRT Displays

Wichmann, F.

In IEEE Visualization, pages: 571-574, (Editors: Moorhead, R.; Gross, M.; Joy, K. I.), IEEE Visualization, 2002 (inproceedings)

Abstract
Most visualization panels today are still built around cathode-ray tubes (CRTs), certainly on personal desktops at work and at home. Whilst capable of producing pleasing images for common applications ranging from email writing to TV and DVD presentation, it is as well to note that there are a number of nonlinear transformations between input (voltage) and output (luminance) which distort the digital and/or analogue images send to a CRT. Some of them are input-independent and hence easy to fix, e.g. gamma correction, but others, such as pixel interactions, depend on the content of the input stimulus and are thus harder to compensate for. CRT-induced image distortions cause problems not only in basic vision research but also for applications where image fidelity is critical, most notably in medicine (digitization of X-ray images for diagnostic purposes) and in forms of online commerce, such as the online sale of images, where the image must be reproduced on some output device which will not have the same transfer function as the customer's CRT. I will present measurements from a number of CRTs and illustrate how some of their shortcomings may be problematic for the aforementioned applications.

[BibTex]

[BibTex]

1996


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The DELVE user manual

Rasmussen, CE., Neal, RM., Hinton, GE., van Camp, D., Revow, M., Ghahramani, Z., Kustra, R., Tibshirani, R.

Department of Computer Science, University of Toronto, December 1996 (techreport)

Abstract
This manual describes the preliminary release of the DELVE environment. Some features described here have not yet implemented, as noted. Support for regression tasks is presently somewhat more developed than that for classification tasks. We recommend that you exercise caution when using this version of DELVE for real work, as it is possible that bugs remain in the software. We hope that you will send us reports of any problems you encounter, as well as any other comments you may have on the software or manual, at the e-mail address below. Please mention the version number of the manual and/or the software with any comments you send.

GZIP [BibTex]

1996

GZIP [BibTex]


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Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Schölkopf, B., Smola, A., Müller, K.

(44), Max Planck Institute for Biological Cybernetics Tübingen, December 1996, This technical report has also been published elsewhere (techreport)

Abstract
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible 5-pixel products in 16 x 16 images. We give the derivation of the method, along with a discussion of other techniques which can be made nonlinear with the kernel approach; and present first experimental results on nonlinear feature extraction for pattern recognition.

[BibTex]

[BibTex]


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Quality Prediction of Steel Products using Neural Networks

Shin, H., Jhee, W.

In Proc. of the Korean Expert System Conference, pages: 112-124, Korean Expert System Society Conference, November 1996 (inproceedings)

[BibTex]

[BibTex]


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Comparison of view-based object recognition algorithms using realistic 3D models

Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., Vetter, T.

In Artificial Neural Networks: ICANN 96, LNCS, vol. 1112, pages: 251-256, Lecture Notes in Computer Science, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996 (inproceedings)

Abstract
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high number of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Learning View Graphs for Robot Navigation

Franz, M., Schölkopf, B., Georg, P., Mallot, H., Bülthoff, H.

(33), Max Planck Institute for Biological Cybernetics, Tübingen,, July 1996 (techreport)

Abstract
We present a purely vision-based scheme for learning a parsimonious representation of an open environment. Using simple exploration behaviours, our system constructs a graph of appropriately chosen views. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. Simulations and robot experiments demonstrate the feasibility of the proposed approach.

[BibTex]

[BibTex]


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Incorporating invariances in support vector learning machines

Schölkopf, B., Burges, C., Vapnik, V.

In Artificial Neural Networks: ICANN 96, LNCS vol. 1112, pages: 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science (inproceedings)

Abstract
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.

PDF DOI [BibTex]

PDF DOI [BibTex]


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A practical Monte Carlo implementation of Bayesian learning

Rasmussen, CE.

In Advances in Neural Information Processing Systems 8, pages: 598-604, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo methods is presented and evaluated. In reasonably small amounts of computer time this approach outperforms other state-of-the-art methods on 5 datalimited tasks from real world domains.

PDF Web [BibTex]

PDF Web [BibTex]


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Gaussian Processes for Regression

Williams, CKI., Rasmussen, CE.

In Advances in neural information processing systems 8, pages: 514-520, (Editors: Touretzky, D.S. , M.C. Mozer, M.E. Hasselmo), MIT Press, Cambridge, MA, USA, Ninth Annual Conference on Neural Information Processing Systems (NIPS), June 1996 (inproceedings)

Abstract
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

PDF Web [BibTex]

PDF Web [BibTex]


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Evaluation of Gaussian Processes and other Methods for Non-Linear Regression

Rasmussen, CE.

Biologische Kybernetik, Graduate Department of Computer Science, Univeristy of Toronto, 1996 (phdthesis)

PostScript [BibTex]

PostScript [BibTex]


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Künstliches Lernen

Schölkopf, B.

In Komplexe adaptive Systeme, Forum für Interdisziplinäre Forschung, 15, pages: 93-117, Forum für interdisziplinäre Forschung, (Editors: S Bornholdt and PH Feindt), Röll, Dettelbach, 1996 (inbook)

[BibTex]

[BibTex]


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Aktives Erwerben eines Ansichtsgraphen zur diskreten Repräsentation offener Umwelten.

Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.

Fortschritte der K{\"u}nstlichen Intelligenz, pages: 138-147, (Editors: M. Thielscher and S.-E. Bornscheuer), 1996 (poster)

PDF PostScript [BibTex]

PDF PostScript [BibTex]


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Does motion-blur facilitate motion detection ?

Wichmann, F., Henning, G.

OSA Conference Program, pages: S127, 1996 (poster)

Abstract
Retinal-image motion induces the perceptual loss of high spatial-frequency content - motion blur - that affects broadband stimuli. The relative detectability of motion blur and motion itself, measured in 2-AFC experiments, shows that, although the blur associated with motion can be detected, motion itself is the more effective cue.

[BibTex]

[BibTex]

1994


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View-based cognitive mapping and path planning

Schölkopf, B., Mallot, H.

(7), Max Planck Institute for Biological Cybernetics Tübingen, November 1994, This technical report has also been published elsewhere (techreport)

Abstract
We present a scheme for learning a cognitive map of a maze from a sequence of views and movement decisions. The scheme is based on an intermediate representation called the view graph. We show that this representation carries sufficient information to reconstruct the topological and directional structure of the maze. Moreover, we present a neural network that learns the view graph during a random exploration of the maze. We use a unsupervised competitive learning rule which translates temporal sequence (rather than similarity) of views into connectedness in the network. The network uses its knowledge of the topological and directional structure of the maze to generate expectations about which views are likely to be perceived next, improving the view recognition performance. We provide an additional mechanism which uses the map to find paths between arbitrary points of the previously explored environment. The results are compared to findings of behavioural neuroscience.

[BibTex]

1994

[BibTex]


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Raman and Infrared-Spectra of Solid Chloroflouromethane

Schlueter, S., Davison, T., Anderson, A.

Journal of Raman Spectroscopy, 25, pages: 429-433, 1994 (article)

Abstract
Raman and infrared spectra of solid CH2CIF (Freon 31) were recorded in both the lattice and internal mode regions for samples at temperatures between 12 and 125 K. No evidence of any solid-state phase transition was found, but some thin-film samples deposited at low temperatures appear to exist in a metastable phase. Spectra of the stable phase are compatible with a non-centrosymmetric unit cell containing four molecules. Lattice peaks are assigned on the basis of geometrical and intensity arguments.

Web [BibTex]

Web [BibTex]


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Pruning from Adaptive Regularization

Hansen, LK., Rasmussen, CE.

Neural Computation, 6(6):1222-1231, 1994 (article)

Abstract
Inspired by the recent upsurge of interest in Bayesian methods we consider adaptive regularization. A generalization based scheme for adaptation of regularization parameters is introduced and compared to Bayesian regularization.We show that pruning arises naturally within both adaptive regularization schemes. As model example we have chosen the simplest possible: estimating the mean of a random variable with known variance. Marked similarities are found between the two methods in that they both involve a "noise limit", below which they regularize with infinite weight decay, i.e., they prune.However, pruning is not always beneficial. We show explicitly that both methods in some cases may increase the generalization error. This corresponds to situations where the underlying assumptions of the regularizer are poorly matched to the environment.

PDF PostScript [BibTex]

PDF PostScript [BibTex]