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2008


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Variational Bayesian Model Selection in Linear Gaussian State-Space based Models

Chiappa, S.

International Workshop on Flexible Modelling: Smoothing and Robustness (FMSR 2008), 2008, pages: 1, November 2008 (poster)

ei

Web [BibTex]

2008


Web [BibTex]


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Towards the neural basis of the flash-lag effect

Ecker, A., Berens, P., Hoenselaar, A., Subramaniyan, M., Tolias, A., Bethge, M.

International Workshop on Aspects of Adaptive Cortex Dynamics, 2008, pages: 1, September 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Policy Learning: A Unified Perspective With Applications In Robotics

Peters, J., Kober, J., Nguyen-Tuong, D.

8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008), 8, pages: 10, July 2008 (poster)

Abstract
Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning al- gorithms from a common point of view, i.e, policy gradient algorithms, natural- gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.

ei

PDF [BibTex]

PDF [BibTex]


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Reinforcement Learning of Perceptual Coupling for Motor Primitives

Kober, J., Peters, J.

8th European Workshop on Reinforcement Learning for Robotics (EWRL 2008), 8, pages: 16, July 2008 (poster)

Abstract
Reinforcement learning is a natural choice for the learning of complex motor tasks by reward-related self-improvement. As the space of movements is high-dimensional and continuous, a policy parametrization is needed which can be used in this context. Traditional motor primitive approaches deal largely with open-loop policies which can only deal with small perturbations. In this paper, we present a new type of motor primitive policies which serve as closed-loop policies together with an appropriate learning algorithm. Our new motor primitives are an augmented version version of the dynamic systems motor primitives that incorporates perceptual coupling to external variables. We show that these motor primitives can perform complex tasks such a Ball-in-a-Cup or Kendama task even with large variances in the initial conditions where a human would hardly be able to learn this task. We initialize the open-loop policies by imitation learning and the perceptual coupling with a handcrafted solution. We first improve the open-loop policies and subsequently the perceptual coupling using a novel reinforcement learning method which is particularly well-suited for motor primitives.

ei

PDF [BibTex]

PDF [BibTex]


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Flexible Models for Population Spike Trains

Bethge, M., Macke, J., Berens, P., Ecker, A., Tolias, A.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 52, June 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Pairwise Correlations and Multineuronal Firing Patterns in the Primary Visual Cortex of the Awake, Behaving Macaque

Berens, P., Ecker, A., Subramaniyan, M., Macke, J., Hauck, P., Bethge, M., Tolias, A.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 48, June 2008 (poster)

ei

PDF [BibTex]

PDF [BibTex]


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Visual saliency re-visited: Center-surround patterns emerge as optimal predictors for human fixation targets

Wichmann, F., Kienzle, W., Schölkopf, B., Franz, M.

Journal of Vision, 8(6):635, 8th Annual Meeting of the Vision Sciences Society (VSS), June 2008 (poster)

Abstract
Humans perceives the world by directing the center of gaze from one location to another via rapid eye movements, called saccades. In the period between saccades the direction of gaze is held fixed for a few hundred milliseconds (fixations). It is primarily during fixations that information enters the visual system. Remarkably, however, after only a few fixations we perceive a coherent, high-resolution scene despite the visual acuity of the eye quickly decreasing away from the center of gaze: This suggests an effective strategy for selecting saccade targets. Top-down effects, such as the observer's task, thoughts, or intentions have an effect on saccadic selection. Equally well known is that bottom-up effects-local image structure-influence saccade targeting regardless of top-down effects. However, the question of what the most salient visual features are is still under debate. Here we model the relationship between spatial intensity patterns in natural images and the response of the saccadic system using tools from machine learning. This allows us to identify the most salient image patterns that guide the bottom-up component of the saccadic selection system, which we refer to as perceptive fields. We show that center-surround patterns emerge as the optimal solution to the problem of predicting saccade targets. Using a novel nonlinear system identification technique we reduce our learned classifier to a one-layer feed-forward network which is surprisingly simple compared to previously suggested models assuming more complex computations such as multi-scale processing, oriented filters and lateral inhibition. Nevertheless, our model is equally predictive and generalizes better to novel image sets. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Analysis of Pattern Recognition Methods in Classifying Bold Signals in Monkeys at 7-Tesla

Ku, S., Gretton, A., Macke, J., Tolias, A., Logothetis, N.

AREADNE 2008: Research in Encoding and Decoding of Neural Ensembles, 2, pages: 67, June 2008 (poster)

Abstract
Pattern recognition methods have shown that fMRI data can reveal significant information about brain activity. For example, in the debate of how object-categories are represented in the brain, multivariate analysis has been used to provide evidence of distributed encoding schemes. Many follow-up studies have employed different methods to analyze human fMRI data with varying degrees of success. In this study we compare four popular pattern recognition methods: correlation analysis, support-vector machines (SVM), linear discriminant analysis and Gaussian naïve Bayes (GNB), using data collected at high field (7T) with higher resolution than usual fMRI studies. We investigate prediction performance on single trials and for averages across varying numbers of stimulus presentations. The performance of the various algorithms depends on the nature of the brain activity being categorized: for several tasks, many of the methods work well, whereas for others, no methods perform above chance level. An important factor in overall classification performance is careful preprocessing of the data, including dimensionality reduction, voxel selection, and outlier elimination.

ei

[BibTex]

[BibTex]


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New Frontiers in Characterizing Structure and Dynamics by NMR

Nilges, M., Markwick, P., Malliavin, TE., Rieping, W., Habeck, M.

In Computational Structural Biology: Methods and Applications, pages: 655-680, (Editors: Schwede, T. , M. C. Peitsch), World Scientific, New Jersey, NJ, USA, May 2008 (inbook)

Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as the method of choice for studying both the structure and the dynamics of biological macromolecule in solution. Despite the maturity of the NMR method for structure determination, its application faces a number of challenges. The method is limited to systems of relatively small molecular mass, data collection times are long, data analysis remains a lengthy procedure, and it is difficult to evaluate the quality of the final structures. The last years have seen significant advances in experimental techniques to overcome or reduce some limitations. The function of bio-macromolecules is determined by both their 3D structure and conformational dynamics. These molecules are inherently flexible systems displaying a broad range of dynamics on time–scales from picoseconds to seconds. NMR is unique in its ability to obtain dynamic information on an atomic scale. The experimental information on structure and dynamics is intricately mixed. It is however difficult to unite both structural and dynamical information into one consistent model, and protocols for the determination of structure and dynamics are performed independently. This chapter deals with the challenges posed by the interpretation of NMR data on structure and dynamics. We will first relate the standard structure calculation methods to Bayesian probability theory. We will then briefly describe the advantages of a fully Bayesian treatment of structure calculation. Then, we will illustrate the advantages of using Bayesian reasoning at least partly in standard structure calculations. The final part will be devoted to interpretation of experimental data on dynamics.

ei

Web [BibTex]

Web [BibTex]


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The role of stimulus correlations for population decoding in the retina

Schwartz, G., Macke, J., Berry, M.

Computational and Systems Neuroscience 2008 (COSYNE 2008), 5, pages: 172, March 2008 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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A Robot System for Biomimetic Navigation: From Snapshots to Metric Embeddings of View Graphs

Franz, MO., Stürzl, W., Reichardt, W., Mallot, HA.

In Robotics and Cognitive Approaches to Spatial Mapping, pages: 297-314, Springer Tracts in Advanced Robotics ; 38, (Editors: Jefferies, M.E. , W.-K. Yeap), Springer, Berlin, Germany, 2008 (inbook)

Abstract
Complex navigation behaviour (way-finding) involves recognizing several places and encoding a spatial relationship between them. Way-finding skills can be classified into a hierarchy according to the complexity of the tasks that can be performed [8]. The most basic form of way-finding is route navigation, followed by topological navigation where several routes are integrated into a graph-like representation. The highest level, survey navigation, is reached when this graph can be embedded into a common reference frame. In this chapter, we present the building blocks for a biomimetic robot navigation system that encompasses all levels of this hierarchy. As a local navigation method, we use scene-based homing. In this scheme, a goal location is characterized either by a panoramic snapshot of the light intensities as seen from the place, or by a record of the distances to the surrounding objects. The goal is found by moving in the direction that minimizes the discrepancy between the recorded intensities or distances and the current sensory input. For learning routes, the robot selects distinct views during exploration that are close enough to be reached by snapshot-based homing. When it encounters already visited places during route learning, it connects the routes and thus forms a topological representation of its environment termed a view graph. The final stage, survey navigation, is achieved by a graph embedding procedure which complements the topologic information of the view graph with odometric position estimates. Calculation of the graph embedding is done with a modified multidimensional scaling algorithm which makes use of distances and angles between nodes.

ei

PDF PDF DOI [BibTex]

PDF PDF DOI [BibTex]


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Hydrogen adsorption (Carbon, Zeolites, Nanocubes)

Hirscher, M., Panella, B.

In Hydrogen as a Future Energy Carrier, pages: 173-188, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, 2008 (incollection)

mms

[BibTex]

[BibTex]


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Ma\ssgeschneiderte Speichermaterialien

Hirscher, M.

In Von Brennstoffzellen bis Leuchtdioden (Energie und Chemie - Ein Bündnis für die Zukunft), pages: 31-33, Deutsche Bunsen-Gesellschaft für Physikalische Chemie e.V., Frankfurt am Main, 2008 (incollection)

mms

[BibTex]

[BibTex]

2006


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Some observations on the pedestal effect or dipper function

Henning, B., Wichmann, F.

Journal of Vision, 6(13):50, 2006 Fall Vision Meeting of the Optical Society of America, December 2006 (poster)

Abstract
The pedestal effect is the large improvement in the detectabilty of a sinusoidal “signal” grating observed when the signal is added to a masking or “pedestal” grating of the same spatial frequency, orientation, and phase. We measured the pedestal effect in both broadband and notched noise - noise from which a 1.5-octave band centred on the signal frequency had been removed. Although the pedestal effect persists in broadband noise, it almost disappears in the notched noise. Furthermore, the pedestal effect is substantial when either high- or low-pass masking noise is used. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies different from that of the signal and pedestal. The spatial-frequency components of the notched noise above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. Thus the pedestal or dipper effect measured without notched noise is not a characteristic of individual spatial-frequency tuned channels.

ei

Web DOI [BibTex]

2006


Web DOI [BibTex]


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Prediction of Protein Function from Networks

Shin, H., Tsuda, K.

In Semi-Supervised Learning, pages: 361-376, Adaptive Computation and Machine Learning, (Editors: Chapelle, O. , B. Schölkopf, A. Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
In computational biology, it is common to represent domain knowledge using graphs. Frequently there exist multiple graphs for the same set of nodes, representing information from different sources, and no single graph is sufficient to predict class labels of unlabelled nodes reliably. One way to enhance reliability is to integrate multiple graphs, since individual graphs are partly independent and partly complementary to each other for prediction. In this chapter, we describe an algorithm to assign weights to multiple graphs within graph-based semi-supervised learning. Both predicting class labels and searching for weights for combining multiple graphs are formulated into one convex optimization problem. The graph-combining method is applied to functional class prediction of yeast proteins.When compared with individual graphs, the combined graph with optimized weights performs significantly better than any single graph.When compared with the semidefinite programming-based support vector machine (SDP/SVM), it shows comparable accuracy in a remarkably short time. Compared with a combined graph with equal-valued weights, our method could select important graphs without loss of accuracy, which implies the desirable property of integration with selectivity.

ei

Web [BibTex]

Web [BibTex]


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Discrete Regularization

Zhou, D., Schölkopf, B.

In Semi-supervised Learning, pages: 237-250, Adaptive computation and machine learning, (Editors: O Chapelle and B Schölkopf and A Zien), MIT Press, Cambridge, MA, USA, November 2006 (inbook)

Abstract
Many real-world machine learning problems are situated on finite discrete sets, including dimensionality reduction, clustering, and transductive inference. A variety of approaches for learning from finite sets has been proposed from different motivations and for different problems. In most of those approaches, a finite set is modeled as a graph, in which the edges encode pairwise relationships among the objects in the set. Consequently many concepts and methods from graph theory are adopted. In particular, the graph Laplacian is widely used. In this chapter we present a systemic framework for learning from a finite set represented as a graph. We develop discrete analogues of a number of differential operators, and then construct a discrete analogue of classical regularization theory based on those discrete differential operators. The graph Laplacian based approaches are special cases of this general discrete regularization framework. An important thing implied in this framework is that we have a wide choices of regularization on graph in addition to the widely-used graph Laplacian based one.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Optimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches

Farquhar, J., Hill, N., Schölkopf, B.

Challenging Brain-Computer Interfaces: MAIA Workshop 2006, pages: 1, November 2006 (poster)

Abstract
We present easy-to-use alternatives to the often-used two-stage Common Spatial Pattern + classifier approach for spatial filtering and classification of Event-Related Desychnronization signals in BCI. We report two algorithms that aim to optimize the spatial filters according to a criterion more directly related to the ability of the algorithms to generalize to unseen data. Both are based upon the idea of treating the spatial filter coefficients as hyperparameters of a kernel or covariance function. We then optimize these hyper-parameters directly along side the normal classifier parameters with respect to our chosen learning objective function. The two objectives considered are margin maximization as used in Support-Vector Machines and the evidence maximization framework used in Gaussian Processes. Our experiments assessed generalization error as a function of the number of training points used, on 9 BCI competition data sets and 5 offline motor imagery data sets measured in Tubingen. Both our approaches sho w consistent improvements relative to the commonly used CSP+linear classifier combination. Strikingly, the improvement is most significant in the higher noise cases, when either few trails are used for training, or with the most poorly performing subjects. This a reversal of the usual "rich get richer" effect in the development of CSP extensions, which tend to perform best when the signal is strong enough to accurately find their additional parameters. This makes our approach particularly suitable for clinical application where high levels of noise are to be expected.

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


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Learning Eye Movements

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M.

Sensory Coding And The Natural Environment, 2006, pages: 1, September 2006 (poster)

Abstract
The human visual system samples images through saccadic eye movements which rapidly change the point of fixation. Although the selection of eye movement targets depends on numerous top-down mechanisms, a number of recent studies have shown that low-level image features such as local contrast or edges play an important role. These studies typically used predefined image features which were afterwards experimentally verified. Here, we follow a complementary approach: instead of testing a set of candidate image features, we infer these hypotheses from the data, using methods from statistical learning. To this end, we train a non-linear classifier on fixated vs. randomly selected image patches without making any physiological assumptions. The resulting classifier can be essentially characterized by a nonlinear combination of two center-surround receptive fields. We find that the prediction performance of this simple model on our eye movement data is indistinguishable from the physiologically motivated model of Itti & Koch (2000) which is far more complex. In particular, we obtain a comparable performance without using any multi-scale representations, long-range interactions or oriented image features.

ei

Web [BibTex]

Web [BibTex]


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Classification of natural scenes: Critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Journal of Vision, 6(6):561, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification (A. Torralba & A. Oliva, Network: Comput. Neural Syst., 2003). We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only (Drewes, Wichmann, Gegenfurtner VSS 2005). We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images (“best animals”, “best distractors” and “worst animals”, “worst distractors”). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced (cf. Wichmann, Rosas, Gegenfurtner, VSS 2005). Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Journal of Vision, 6(6):194, 6th Annual Meeting of the Vision Sciences Society (VSS), June 2006 (poster)

Abstract
The pedestal or dipper effect is the large improvement in the detectabilty of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold‘ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5- octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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The Pedestal Effect is Caused by Off-Frequency Looking, not Nonlinear Transduction or Contrast Gain-Control

Wichmann, F., Henning, G.

9, pages: 174, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when the signal is added to a pedestal or masking grating having the signal‘s spatial frequency, orientation, and phase. The effect is largest with pedestal contrasts just above the ‘threshold’ in the absence of a pedestal. We measured the pedestal effect in both broadband and notched masking noise---noise from which a 1.5-octave band centered on the signal and pedestal frequency had been removed. The pedestal effect persists in broadband noise, but almost disappears with notched noise. The spatial-frequency components of the notched noise that lie above and below the spatial frequency of the signal and pedestal prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are very much different from that of the signal and pedestal. We conclude that the pedestal effect in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities at spatial frequencies that are different from that of the signal and pedestal. Thus the pedestal or dipper effect is not a characteristic of individual spatial-frequency tuned channels.

ei

Web [BibTex]

Web [BibTex]


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Classification of Natural Scenes: Critical Features Revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

9, pages: 92, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Despite the seeming complexity of such decisions it has been hypothesized that a simple global image feature, the relative abundance of high spatial frequencies at certain orientations, could underly such fast image classification [1]. We successfully used linear discriminant analysis to classify a set of 11.000 images into “animal” and “non-animal” images based on their individual amplitude spectra only [2]. We proceeded to sort the images based on the performance of our classifier, retaining only the best and worst classified 400 images ("best animals", "best distractors" and "worst animals", "worst distractors"). We used a Go/No-go paradigm to evaluate human performance on this subset of our images. Both reaction time and proportion of correctly classified images showed a significant effect of classification difficulty. Images more easily classified by our algorithm were also classified faster and better by humans, as predicted by the Torralba & Oliva hypothesis. We then equated the amplitude spectra of the 400 images, which, by design, reduced algorithmic performance to chance whereas human performance was only slightly reduced [3]. Most importantly, the same images as before were still classified better and faster, suggesting that even in the original condition features other than specifics of the amplitude spectrum made particular images easy to classify, clearly at odds with the Torralba & Oliva hypothesis.

ei

Web [BibTex]

Web [BibTex]


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Factorial Coding of Natural Images: How Effective are Linear Models in Removing Higher-Order Dependencies?

Bethge, M.

9, pages: 90, 9th T{\"u}bingen Perception Conference (TWK), March 2006 (poster)

Abstract
The performance of unsupervised learning models for natural images is evaluated quantitatively by means of information theory. We estimate the gain in statistical independence (the multi-information reduction) achieved with independent component analysis (ICA), principal component analysis (PCA), zero-phase whitening, and predictive coding. Predictive coding is translated into the transform coding framework, where it can be characterized by the constraint of a triangular filter matrix. A randomly sampled whitening basis and the Haar wavelet are included into the comparison as well. The comparison of all these methods is carried out for different patch sizes, ranging from 2x2 to 16x16 pixels. In spite of large differences in the shape of the basis functions, we find only small differences in the multi-information between all decorrelation transforms (5% or less) for all patch sizes. Among the second-order methods, PCA is optimal for small patch sizes and predictive coding performs best for large patch sizes. The extra gain achieved with ICA is always less than 2%. In conclusion, the `edge filters‘ found with ICA lead only to a surprisingly small improvement in terms of its actual objective.

ei

Web [BibTex]

Web [BibTex]


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Classification of natural scenes: critical features revisited

Drewes, J., Wichmann, F., Gegenfurtner, K.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 251, 2006 (poster)

ei

[BibTex]

[BibTex]


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Texture and haptic cues in slant discrimination: combination is sensitive to reliability but not statistically optimal

Rosas, P., Wagemans, J., Ernst, M., Wichmann, F.

Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen (TeaP 2006), 48, pages: 80, 2006 (poster)

ei

[BibTex]

[BibTex]


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Combining a Filter Method with SVMs

Lal, T., Chapelle, O., Schölkopf, B.

In Feature Extraction: Foundations and Applications, Studies in Fuzziness and Soft Computing, Vol. 207, pages: 439-446, Studies in Fuzziness and Soft Computing ; 207, (Editors: I Guyon and M Nikravesh and S Gunn and LA Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


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Embedded methods

Lal, T., Chapelle, O., Weston, J., Elisseeff, A.

In Feature Extraction: Foundations and Applications, pages: 137-165, Studies in Fuzziness and Soft Computing ; 207, (Editors: Guyon, I. , S. Gunn, M. Nikravesh, L. A. Zadeh), Springer, Berlin, Germany, 2006 (inbook)

Abstract
Embedded methods are a relatively new approach to feature selection. Unlike filter methods, which do not incorporate learning, and wrapper approaches, which can be used with arbitrary classifiers, in embedded methods the features selection part can not be separated from the learning part. Existing embedded methods are reviewed based on a unifying mathematical framework.

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Ähnlichkeitsmasse in Modellen zur Kategorienbildung

Jäkel, F., Wichmann, F.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 223, 2006 (poster)

ei

[BibTex]

[BibTex]


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The pedestal effect is caused by off-frequency looking, not nonlinear transduction or contrast gain-control

Wichmann, F., Henning, B.

Experimentelle Psychologie: Beitr{\"a}ge zur 48. Tagung experimentell arbeitender Psychologen, 48, pages: 205, 2006 (poster)

ei

[BibTex]

[BibTex]


Thumb xl 1 4020 4850 5 1
NONLINEAR OPTICAL PROPERTIES OF CHIRAL LIQUIDS Electric-dipolar pseudoscalars in nonlinear optics

Fischer, P., Champagne, B.

In NON-LINEAR OPTICAL PROPERTIES OF MATTER: FROM MOLECULES TO CONDENSED PHASES, 1, pages: 359-381, Challenges and Advances in Computational Chemistry and Physics, 2006 (incollection)

Abstract
We give all overview of linear and nonlinear optical processes that can be specific to chiral molecules in isotropic media. Specifically, we discuss the pseudoscalars that underlie nonlinear optical activity and chiral frequency conversion processes in fluids. We show that nonlinear optical techniques open entirely new ways of exploring chirality: Sum-frequency-generation (SFG) at second-order and BioCARS at fourth-order arise in the electric-dipole approximation and do not require circularly polarized light to detect chiral molecules in solution. Here the frequency conversion in itself is a measure of chirality. This is in contrast to natural optical activity phenomena which are based on the interference of radiation from induced oscillating electric and magnetic dipoles, and which are observed as a differential response to right and left circularly polarized light. We give examples from our SFG experiments in optically active solutions and show how the application of an additional static electric field to sum-frequency generation allows the absolute configuration of the chiral solute to be determined via all electric-dipolar process. Results from ab initio calculations of the SFG pseudoscalar are presented for a number of chiral molecules

pf

[BibTex]

[BibTex]


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Approximate nearest neighbor regression in very high dimensions

Vijayakumar, S., DSouza, A., Schaal, S.

In Nearest-Neighbor Methods in Learning and Vision, pages: 103-142, (Editors: Shakhnarovich, G.;Darrell, T.;Indyk, P.), Cambridge, MA: MIT Press, 2006, clmc (inbook)

am

link (url) [BibTex]

link (url) [BibTex]

2005


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Kernel methods for dependence testing in LFP-MUA

Gretton, A., Belitski, A., Murayama, Y., Schölkopf, B., Logothetis, N.

35(689.17), 35th Annual Meeting of the Society for Neuroscience (Neuroscience), November 2005 (poster)

Abstract
A fundamental problem in neuroscience is determining whether or not particular neural signals are dependent. The correlation is the most straightforward basis for such tests, but considerable work also focuses on the mutual information (MI), which is capable of revealing dependence of higher orders that the correlation cannot detect. That said, there are other measures of dependence that share with the MI an ability to detect dependence of any order, but which can be easier to compute in practice. We focus in particular on tests based on the functional covariance, which derive from work originally accomplished in 1959 by Renyi. Conceptually, our dependence tests work by computing the covariance between (infinite dimensional) vectors of nonlinear mappings of the observations being tested, and then determining whether this covariance is zero - we call this measure the constrained covariance (COCO). When these vectors are members of universal reproducing kernel Hilbert spaces, we can prove this covariance to be zero only when the variables being tested are independent. The greatest advantage of these tests, compared with the mutual information, is their simplicity – when comparing two signals, we need only take the largest eigenvalue (or the trace) of a product of two matrices of nonlinearities, where these matrices are generally much smaller than the number of observations (and are very simple to construct). We compare the mutual information, the COCO, and the correlation in the context of finding changes in dependence between the LFP and MUA signals in the primary visual cortex of the anaesthetized macaque, during the presentation of dynamic natural stimuli. We demonstrate that the MI and COCO reveal dependence which is not detected by the correlation alone (which we prove by artificially removing all correlation between the signals, and then testing their dependence with COCO and the MI); and that COCO and the MI give results consistent with each other on our data.

ei

Web [BibTex]

2005


Web [BibTex]


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Rapid animal detection in natural scenes: Critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Journal of Vision, 5(8):376, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract
Thorpe et al (Nature 381, 1996) first showed how rapidly human observers are able to classify natural images as to whether they contain an animal or not. Whilst the basic result has been replicated using different response paradigms (yes-no versus forced-choice), modalities (eye movements versus button presses) as well as while measuring neurophysiological correlates (ERPs), it is still unclear which image features support this rapid categorisation. Recently Torralba and Oliva (Network: Computation in Neural Systems, 14, 2003) suggested that simple global image statistics can be used to predict seemingly complex decisions about the absence and/or presence of objects in natural scences. They show that the information contained in a small number (N=16) of spectral principal components (SPC)—principal component analysis (PCA) applied to the normalised power spectra of the images—is sufficient to achieve approximately 80% correct animal detection in natural scenes. Our goal was to test whether human observers make use of the power spectrum when rapidly classifying natural scenes. We measured our subjects' ability to detect animals in natural scenes as a function of presentation time (13 to 167 msec); images were immediately followed by a noise mask. In one condition we used the original images, in the other images whose power spectra were equalised (each power spectrum was set to the mean power spectrum over our ensemble of 1476 images). Thresholds for 75% correct animal detection were in the region of 20–30 msec for all observers, independent of the power spectrum of the images: this result makes it very unlikely that human observers make use of the global power spectrum. Taken together with the results of Gegenfurtner, Braun & Wichmann (Journal of Vision [abstract], 2003), showing the robustness of animal detection to global phase noise, we conclude that humans use local features, like edges and contours, in rapid animal detection.

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Learning an Interest Operator from Eye Movements

Kienzle, W., Franz, M., Wichmann, F., Schölkopf, B.

International Workshop on Bioinspired Information Processing (BIP 2005), 2005, pages: 1, September 2005 (poster)

ei

PDF Web [BibTex]

PDF Web [BibTex]


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Classification of natural scenes using global image statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

Journal of Vision, 5(8):602, Fifth Annual Meeting of the Vision Sciences Society (VSS), September 2005 (poster)

Abstract
The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and non-animal images. After applying a DFT to the image, we put the Fourier spectrum into bins (8 orientations with 6 frequency bands each). Using all bins, classification performance on the Fourier spectrum reached 70%. However, performance was similar (67%) when only the high spatial frequency information was used and decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially filtered images. A detailed analysis of the classification weights showed that a relatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical and horizontal orientation at the highest spatial frequency band. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

ei

Web DOI [BibTex]

Web DOI [BibTex]


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Comparative evaluation of Independent Components Analysis algorithms for isolating target-relevant information in brain-signal classification

Hill, N., Schröder, M., Lal, T., Schölkopf, B.

Brain-Computer Interface Technology, 3, pages: 95, June 2005 (poster)

ei

PDF [BibTex]


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Classification of natural scenes using global image statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

47, pages: 88, 47. Tagung Experimentell Arbeitender Psychologen, April 2005 (poster)

ei

[BibTex]

[BibTex]


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Classification of Natural Scenes using Global Image Statistics

Drewes, J., Wichmann, F., Gegenfurtner, K.

8, pages: 88, 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
The algorithmic classification of complex, natural scenes is generally considered a difficult task due to the large amount of information conveyed by natural images. Work by Simon Thorpe and colleagues showed that humans are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. This suggests that the relevant information for classification can be extracted at comparatively limited computational cost. One hypothesis is that global image statistics such as the amplitude spectrum could underly fast image classification (Johnson & Olshausen, Journal of Vision, 2003; Torralba & Oliva, Network: Comput. Neural Syst., 2003). We used linear discriminant analysis to classify a set of 11.000 images into animal and nonanimal images. After applying a DFT to the image, we put the Fourier spectrum of each image into 48 bins (8 orientations with 6 frequency bands). Using all of these bins, classification performance on the Fourier spectrum reached 70%. In an iterative procedure, we then removed the bins whose absence caused the smallest damage to the classification performance (one bin per iteration). Notably, performance stayed at about 70% until less then 6 bins were left. A detailed analysis of the classification weights showed that a comparatively high level of performance (67%) could also be obtained when only 2 bins were used, namely the vertical orientations at the highest spatial frequency band. When using only a single frequency band (8 bins) we found that 67% classification performance could be reached when only the high spatial frequency information was used, which decreased steadily at lower spatial frequencies, reaching a minimum (50%) for the low spatial frequency information. Similar results were obtained when all bins were used on spatially pre-filtered images. Our results show that in the absence of sophisticated machine learning techniques, animal detection in natural scenes is limited to rather modest levels of performance, far below those of human observers. If limiting oneself to global image statistics such as the DFT then mostly information at the highest spatial frequencies is useful for the task. This is analogous to the results obtained with human observers on filtered images (Kirchner et al, VSS 2004).

ei

Web [BibTex]

Web [BibTex]


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Efficient Adaptive Sampling of the Psychometric Function by Maximizing Information Gain

Tanner, T., Hill, N., Rasmussen, C., Wichmann, F.

8, pages: 109, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
A psychometric function can be described by its shape and four parameters: position or threshold, slope or width, false alarm rate or chance level, and miss or lapse rate. Depending on the parameters of interest some points on the psychometric function may be more informative than others. Adaptive methods attempt to place trials on the most informative points based on the data collected in previous trials. We introduce a new adaptive bayesian psychometric method which collects data for any set of parameters with high efficency. It places trials by minimizing the expected entropy [1] of the posterior pdf over a set of possible stimuli. In contrast to most other adaptive methods it is neither limited to threshold measurement nor to forced-choice designs. Nuisance parameters can be included in the estimation and lead to less biased estimates. The method supports block designs which do not harm the performance when a sufficient number of trials are performed. Block designs are useful for control of response bias and short term performance shifts such as adaptation. We present the results of evaluations of the method by computer simulations and experiments with human observers. In the simulations we investigated the role of parametric assumptions, the quality of different point estimates, the effect of dynamic termination criteria and many other settings. [1] Kontsevich, L.L. and Tyler, C.W. (1999): Bayesian adaptive estimation of psychometric slope and threshold. Vis. Res. 39 (16), 2729-2737.

ei

Web [BibTex]

Web [BibTex]


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Bayesian Inference for Psychometric Functions

Kuss, M., Jäkel, F., Wichmann, F.

8, pages: 106, (Editors: Bülthoff, H. H., H. A. Mallot, R. Ulrich and F. A. Wichmann), 8th T{\"u}bingen Perception Conference (TWK), February 2005 (poster)

Abstract
In psychophysical studies of perception the psychometric function is used to model the relation between the physical stimulus intensity and the observer's ability to detect or discriminate between stimuli of different intensities. We propose the use of Bayesian inference to extract the information contained in experimental data to learn about the parameters of psychometric functions. Since Bayesian inference cannot be performed analytically we use a Markov chain Monte Carlo method to generate samples from the posterior distribution over parameters. These samples can be used to estimate Bayesian confidence intervals and other characteristics of the posterior distribution. We compare our approach with traditional methods based on maximum-likelihood parameter estimation combined with parametric bootstrap techniques for confidence interval estimation. Experiments indicate that Bayesian inference methods are superior to bootstrap-based methods and are thus the method of choice for estimating the psychometric function and its confidence-intervals.

ei

Web [BibTex]

Web [BibTex]


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Global image statistics of natural scenes

Drewes, J., Wichmann, F., Gegenfurtner, K.

Bioinspired Information Processing, 08, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]


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Support Vector Machines and Kernel Algorithms

Schölkopf, B., Smola, A.

In Encyclopedia of Biostatistics (2nd edition), Vol. 8, 8, pages: 5328-5335, (Editors: P Armitage and T Colton), John Wiley & Sons, NY USA, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Visual perception I: Basic principles

Wagemans, J., Wichmann, F., de Beeck, H.

In Handbook of Cognition, pages: 3-47, (Editors: Lamberts, K. , R. Goldstone), Sage, London, 2005 (inbook)

ei

[BibTex]

[BibTex]


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Kernel-Methods, Similarity, and Exemplar Theories of Categorization

Jäkel, F., Wichmann, F.

ASIC, 4, 2005 (poster)

Abstract
Kernel-methods are popular tools in machine learning and statistics that can be implemented in a simple feed-forward neural network. They have strong connections to several psychological theories. For example, Shepard‘s universal law of generalization can be given a kernel interpretation. This leads to an inner product and a metric on the psychological space that is different from the usual Minkowski norm. The metric has psychologically interesting properties: It is bounded from above and does not have additive segments. As categorization models often rely on Shepard‘s law as a model for psychological similarity some of them can be recast as kernel-methods. In particular, ALCOVE is shown to be closely related to kernel logistic regression. The relationship to the Generalized Context Model is also discussed. It is argued that functional analysis which is routinely used in machine learning provides valuable insights also for psychology.

ei

Web [BibTex]


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Rapid animal detection in natural scenes: critical features are local

Wichmann, F., Rosas, P., Gegenfurtner, K.

Experimentelle Psychologie. Beitr{\"a}ge zur 47. Tagung experimentell arbeitender Psychologen, 47, pages: 225, 2005 (poster)

ei

[BibTex]

[BibTex]


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The human brain as large margin classifier

Graf, A., Wichmann, F., Bülthoff, H., Schölkopf, B.

Proceedings of the Computational & Systems Neuroscience Meeting (COSYNE), 2, pages: 1, 2005 (poster)

ei

[BibTex]

[BibTex]


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Geckobot and waalbot: Small-scale wall climbing robots

Unver, O., Murphy, M., Sitti, M.

In Infotech@ Aerospace, pages: 6940, 2005 (incollection)

pi

[BibTex]

[BibTex]

2003


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Texture and haptic cues in slant discrimination: Measuring the effect of texture type on cue combination

Rosas, P., Wichmann, F., Ernst, M., Wagemans, J.

Journal of Vision, 3(12):26, 2003 Fall Vision Meeting of the Optical Society of America, December 2003 (poster)

Abstract
In a number of models of depth cue combination the depth percept is constructed via a weighted average combination of independent depth estimations. The influence of each cue in such average depends on the reliability of the source of information. (Young, Landy, & Maloney, 1993; Ernst & Banks, 2002.) In particular, Ernst & Banks (2002) formulate the combination performed by the human brain as that of the minimum variance unbiased estimator that can be constructed from the available cues. Using slant discrimination and slant judgment via probe adjustment as tasks, we have observed systematic differences in performance of human observers when a number of different types of textures were used as cue to slant (Rosas, Wichmann & Wagemans, 2003). If the depth percept behaves as described above, our measurements of the slopes of the psychometric functions provide the predicted weights for the texture cue for the ranked texture types. We have combined these texture types with object motion but the obtained results are difficult to reconcile with the unbiased minimum variance estimator model (Rosas & Wagemans, 2003). This apparent failure of such model might be explained by the existence of a coupling of texture and motion, violating the assumption of independence of cues. Hillis, Ernst, Banks, & Landy (2002) have shown that while for between-modality combination the human visual system has access to the single-cue information, for within-modality combination (visual cues: disparity and texture) the single-cue information is lost, suggesting a coupling between these cues. Then, in the present study we combine the different texture types with haptic information in a slant discrimination task, to test whether in the between-modality condition the texture cue and the haptic cue to slant are combined as predicted by an unbiased, minimum variance estimator model.

ei

Web DOI [BibTex]

2003


Web DOI [BibTex]