Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

85 results (View BibTeX file of all listed publications)

2018

Representation of sensory uncertainty in macaque visual cortex

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

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

2018

Goris, R., Henaff, O., Meding, K. Representation of sensory uncertainty in macaque visual cortex Computational and Systems Neuroscience (COSYNE) 2018, March 2018 (poster)

[BibTex]

A virtual reality environment for experiments in assistive robotics and neural interfaces

Bustamante, S.

Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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Bustamante, S. A virtual reality environment for experiments in assistive robotics and neural interfaces Graduate School of Neural Information Processing, Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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Optimal Trajectory Generation and Learning Control for Robot Table Tennis

Koc, O.

Technical University Darmstadt, Germany, 2018 (phdthesis)

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Koc, O. Optimal Trajectory Generation and Learning Control for Robot Table Tennis Technical University Darmstadt, Germany, 2018 (phdthesis)

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On the Applicability of Machine Learning to Aid the Search for Gravitational Waves at the LIGO Experiment

Gebhard, T.

Karlsruhe Institute of Technology, Germany, 2018 (mastersthesis)

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Gebhard, T. On the Applicability of Machine Learning to Aid the Search for Gravitational Waves at the LIGO Experiment Karlsruhe Institute of Technology, Germany, 2018 (mastersthesis)

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Distribution-Dissimilarities in Machine Learning

Simon-Gabriel, C. J.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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Simon-Gabriel, C. J. Distribution-Dissimilarities in Machine Learning Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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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]

Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N. K., Besserve, M. Generalized phase locking analysis of electrophysiology data 7th AREADNE Conference on Research in Encoding and Decoding of Neural Ensembles, 2018 (poster)

link (url) Project Page [BibTex]

Domain Adaptation Under Causal Assumptions

Lechner, T.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

[BibTex]

Lechner, T. Domain Adaptation Under Causal Assumptions Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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A Causal Perspective on Deep Representation Learning

Suter, R.

ETH Zurich, 2018 (mastersthesis)

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Suter, R. A Causal Perspective on Deep Representation Learning ETH Zurich, 2018 (mastersthesis)

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Probabilistic Approaches to Stochastic Optimization

Mahsereci, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

Mahsereci, M. Probabilistic Approaches to Stochastic Optimization Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

link (url) Project Page [BibTex]

Improving Tissue Differentiation based on Optical Emission Spectroscopy for Guided Electrosurgical Tumor Resection with Machine Learning

Zabel, S.

Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

[BibTex]

Zabel, S. Improving Tissue Differentiation based on Optical Emission Spectroscopy for Guided Electrosurgical Tumor Resection with Machine Learning Eberhard Karls Universität Tübingen, Germany, 2018 (mastersthesis)

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Reinforcement Learning for High-Speed Robotics with Muscular Actuation

Guist, S.

Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

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Guist, S. Reinforcement Learning for High-Speed Robotics with Muscular Actuation Ruprecht-Karls-Universität Heidelberg , 2018 (mastersthesis)

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Photorealistic Video Super Resolution

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

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

[BibTex]

Pérez-Pellitero, E., Sajjadi, M. S. M., Hirsch, M., Schölkopf, B. Photorealistic Video Super Resolution Workshop and Challenge on Perceptual Image Restoration and Manipulation (PIRM) at the 15th European Conference on Computer Vision (ECCV), 2018 (poster)

[BibTex]

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)

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Meding, K., Hirsch, M., Wichmann, F. A. Retinal image quality of the human eye across the visual field 14th Biannual Conference of the German Society for Cognitive Science (KOGWIS 2018), 2018 (poster)

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Probabilistic Ordinary Differential Equation Solvers — Theory and Applications

Schober, M.

Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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Schober, M. Probabilistic Ordinary Differential Equation Solvers — Theory and Applications Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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

Jayaram, V.

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

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Jayaram, V. A machine learning approach to taking EEG-based computer interfaces out of the lab Graduate Training Centre of Neuroscience, IMPRS, Eberhard Karls Universität Tübingen, Germany, 2018 (phdthesis)

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2007

Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism

Saigo, H., Hattori, M., Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83% accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.

Web [BibTex]

2007

Saigo, H., Hattori, M., Tsuda, K. Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

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Sonnenburg, S., Zien, A., Philips, P., Rätsch, G. Positional Oligomer Importance Matrices NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G. Machine Learning Algorithms for Polymorphism Detection NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM)method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets, and show that we perform better than the current state of the art. Furthermore, our method provides some insights as to which features are most useful for determining subcellular localization, which are in agreement with biological reasoning.

Web [BibTex]

Zien, A., Ong, C. An Automated Combination of Kernels for Predicting Protein Subcellular Localization NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration

Hill, NJ.

Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

Abstract

I‘ll present a perspective on Brain-Computer Interface development from T{\"u}bingen. Some of the benefits promised by BCI technology lie in the near foreseeable future, and some further away. Our motivation is to make BCI technology feasible for the people who could benefit from what it has to offer soon: namely, people in the "completely locked-in" state. I‘ll mention some of the challenges of working with this user group, and explain the specific directions they have motivated us to take in developing experimental methods, algorithms, and software.

[BibTex]

Hill, NJ. Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

[BibTex]

MR-Based PET Attenuation Correction: Method and Validation

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

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

Abstract

PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

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Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Method and Validation 2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M16-6):1-2, November 2007 (poster)

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Estimating receptive fields without spike-triggering

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

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

Web [BibTex]

Macke, J., Zeck, G., Bethge, M. Estimating receptive fields without spike-triggering 37th annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37(768.1):1, November 2007 (poster)

Web [BibTex]

Evaluation of Deformable Registration Methods for MR-CT Atlas Alignment

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

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

Abstract

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

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Scheel, V., Hofmann, M., Rehfeld, N., Judenhofer, M., Claussen, C., Pichler, B. Evaluation of Deformable Registration Methods for MR-CT Atlas Alignment 2007 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007), 2007(M13-121):1, November 2007 (poster)

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Some Theoretical Aspects of Human Categorization Behavior: Similarity and Generalization

Jäkel, F.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007, passed with "ausgezeichnet", summa cum laude, published online (phdthesis)

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Jäkel, F. Some Theoretical Aspects of Human Categorization Behavior: Similarity and Generalization Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007, passed with "ausgezeichnet", summa cum laude, published online (phdthesis)

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Statistical Learning Theory Approaches to Clustering

Jegelka, S.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007 (diplomathesis)

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Jegelka, S. Statistical Learning Theory Approaches to Clustering Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, November 2007 (diplomathesis)

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

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

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

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Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M., Logothetis, N., Panzeri, S. A time/frequency decomposition of information transmission by LFPs and spikes in the primary visual cortex 37th Annual Meeting of the Society for Neuroscience (Neuroscience 2007), 37, pages: 1, November 2007 (poster)

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Mining expression-dependent modules in the human interaction network

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

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

PDF DOI [BibTex]

Georgii, E., Dietmann, S., Uno, T., Pagel, P., Tsuda, K. Mining expression-dependent modules in the human interaction network BMC Bioinformatics, 8(Suppl. 8):S4, November 2007 (poster)

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Policy Learning for Robotics

Peters, J.

14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

Web [BibTex]

Peters, J. Policy Learning for Robotics 14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

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Hilbert Space Representations of Probability Distributions

Gretton, A.

2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

Abstract

Many problems in unsupervised learning require the analysis of features of probability distributions. At the most fundamental level, we might wish to determine whether two distributions are the same, based on samples from each - this is known as the two-sample or homogeneity problem. We use kernel methods to address this problem, by mapping probability distributions to elements in a reproducing kernel Hilbert space (RKHS). Given a sufficiently rich RKHS, these representations are unique: thus comparing feature space representations allows us to compare distributions without ambiguity. Applications include testing whether cancer subtypes are distinguishable on the basis of DNA microarray data, and whether low frequency oscillations measured at an electrode in the cortex have a different distribution during a neural spike. A more difficult problem is to discover whether two random variables drawn from a joint distribution are independent. It turns out that any dependence between pairs of random variables can be encoded in a cross-covariance operator between appropriate RKHS representations of the variables, and we may test independence by looking at a norm of the operator. We demonstrate this independence test by establishing dependence between an English text and its French translation, as opposed to French text on the same topic but otherwise unrelated. Finally, we show that this operator norm is itself a difference in feature means.

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Gretton, A. Hilbert Space Representations of Probability Distributions 2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

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Regression with Intervals

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A. Regression with Intervals International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

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A Hilbert Space Embedding for Distributions

Smola, A., Gretton, A., Song, L., Schölkopf, B.

Proceedings of the 10th International Conference on Discovery Science (DS 2007), 10, pages: 40-41, October 2007 (poster)

Abstract

While kernel methods are the basis of many popular techniques in supervised learning, they are less commonly used in testing, estimation, and analysis of probability distributions, where information theoretic approaches rule the roost. However it becomes difficult to estimate mutual information or entropy if the data are high dimensional.

PDF PDF DOI [BibTex]

Smola, A., Gretton, A., Song, L., Schölkopf, B. A Hilbert Space Embedding for Distributions Proceedings of the 10th International Conference on Discovery Science (DS 2007), 10, pages: 40-41, October 2007 (poster)

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MR-Based PET Attenuation Correction: Method and Validation

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

Joint Molecular Imaging Conference, September 2007 (talk)

Abstract

PDF Web [BibTex]

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Method and Validation Joint Molecular Imaging Conference, September 2007 (talk)

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Studying the effects of noise correlations on population coding using a sampling method

Ecker, A., Berens, P., Bethge, M., Logothetis, N., Tolias, A.

Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 21, September 2007 (poster)

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Ecker, A., Berens, P., Bethge, M., Logothetis, N., Tolias, A. Studying the effects of noise correlations on population coding using a sampling method Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 21, September 2007 (poster)

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Bayesian methods for NMR structure determination

Habeck, M.

29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

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Habeck, M. Bayesian methods for NMR structure determination 29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

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Near-Maximum Entropy Models for Binary Neural Representations of Natural Images

Berens, P., Bethge, M.

Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 19, September 2007 (poster)

Abstract

Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer from their poor scalability to high dimensions. In sensory coding, however, high-dimensional data is ubiquitous. Here, we introduce a new approach using a near-maximum entropy model, that makes this type of analysis feasible for very high-dimensional data---the model parameters can be derived in closed form and sampling is easy. We demonstrate its usefulness by studying a simple neural representation model of natural images. For the first time, we are able to directly compare predictions from a pairwise maximum entropy model not only in small groups of neurons, but also in larger populations of more than thousand units. Our results indicate that in such larger networks interactions exist that are not predicted by pairwise correlations, despite the fact that pairwise correlations explain the lower-dimensional marginal statistics extrem ely well up to the limit of dimensionality where estimation of the full joint distribution is feasible.

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Berens, P., Bethge, M. Near-Maximum Entropy Models for Binary Neural Representations of Natural Images Neural Coding, Computation and Dynamics (NCCD 07), 1, pages: 19, September 2007 (poster)

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Collaborative Filtering via Ensembles of Matrix Factorizations

Wu, M.

KDD Cup and Workshop, August 2007 (talk)

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Wu, M. Collaborative Filtering via Ensembles of Matrix Factorizations KDD Cup and Workshop, August 2007 (talk)

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Thinking Out Loud: Research and Development of Brain Computer Interfaces

Hill, NJ.

Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

Abstract

My principal interest is in applying machine-learning methods to the development of Brain-Computer Interfaces (BCI). This involves the classification of a user‘s intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced Amyotrophic Lateral Sclerosis) could use to communicate. Such systems have the potential to improve the lives of many people who would be otherwise completely unable to communicate, but they are still very much in the research and development stages.

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Hill, NJ. Thinking Out Loud: Research and Development of Brain Computer Interfaces Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

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Error Correcting Codes for the P300 Visual Speller

Biessmann, F.

Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2007 (diplomathesis)

Abstract

The aim of brain-computer interface (BCI) research is to establish a communication system based on intentional modulation of brain activity. This is accomplished by classifying patterns of brain ac- tivity, volitionally induced by the user. The BCI presented in this study is based on a classical paradigm as proposed by (Farwell and Donchin, 1988), the P300 visual speller. Recording electroencephalo- grams (EEG) from the scalp while presenting letters successively to the user, the speller can infer from the brain signal which letter the user was focussing on. Since EEG recordings are noisy, usually many repetitions are needed to detect the correct letter. The focus of this study was to improve the accuracy of the visual speller applying some basic principles from information theory: Stimulus sequences of the speller have been modi&amp;amp;#64257;ed into error-correcting codes. Additionally a language model was incorporated into the probabilistic letter de- coder. Classi&amp;amp;#64257;cation of single EEG epochs was less accurate using error correcting codes. However, the novel code could compensate for that such that overall, letter accuracies were as high as or even higher than for classical stimulus codes. In particular at high noise levels, error-correcting decoding achieved higher letter accuracies.

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Biessmann, F. Error Correcting Codes for the P300 Visual Speller Biologische Kybernetik, Eberhard-Karls-Universität Tübingen, Tübingen, Germany, July 2007 (diplomathesis)

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Local Learning Algorithms for Transductive Classification, Clustering and Data Projection

Wu, M.

Yahoo Inc., July 2007 (talk)

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Wu, M. Local Learning Algorithms for Transductive Classification, Clustering and Data Projection Yahoo Inc., July 2007 (talk)

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Learning the Influence of Spatio-Temporal Variations in Local Image Structure on Visual Saliency

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

10th T{\"u}binger Wahrnehmungskonferenz (TWK 2007), 10, pages: 1, July 2007 (poster)

Abstract

Computational models for bottom-up visual attention traditionally consist of a bank of Gabor-like or Difference-of-Gaussians filters and a nonlinear combination scheme which combines the filter responses into a real-valued saliency measure [1]. Recently it was shown that a standard machine learning algorithm can be used to derive a saliency model from human eye movement data with a very small number of additional assumptions. The learned model is much simpler than previous models, but nevertheless has state-of-the-art prediction performance [2]. A central result from this study is that DoG-like center-surround filters emerge as the unique solution to optimizing the predictivity of the model. Here we extend the learning method to the temporal domain. While the previous model [2] predicts visual saliency based on local pixel intensities in a static image, our model also takes into account temporal intensity variations. We find that the learned model responds strongly to temporal intensity changes ocurring 200-250ms before a saccade is initiated. This delay coincides with the typical saccadic latencies, indicating that the learning algorithm has extracted a meaningful statistic from the training data. In addition, we show that the model correctly predicts a significant proportion of human eye movements on previously unseen test data.

Web [BibTex]

Kienzle, W., Wichmann, F., Schölkopf, B., Franz, M. Learning the Influence of Spatio-Temporal Variations in Local Image Structure on Visual Saliency 10th T{\"u}binger Wahrnehmungskonferenz (TWK 2007), 10, pages: 1, July 2007 (poster)

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Data-driven goodness-of-fit tests

Langovoy, MA.

Biologische Kybernetik, Georg-August-Universität Göttingen, Göttingen, Germany, July 2007 (phdthesis)

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Langovoy, MA. Data-driven goodness-of-fit tests Biologische Kybernetik, Georg-August-Universität Göttingen, Göttingen, Germany, July 2007 (phdthesis)

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Dirichlet Process Mixtures of Factor Analysers

Görür, D., Rasmussen, C.

Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

Abstract

Mixture of factor analysers (MFA) is a well-known model that combines the dimensionality reduction technique of Factor Analysis (FA) with mixture modeling. The key issue in MFA is deciding on the latent dimension and the number of mixture components to be used. The Bayesian treatment of MFA has been considered by Beal and Ghahramani (2000) using variational approximation and by Fokoué and Titterington (2003) using birth-and death Markov chain Monte Carlo (MCMC). Here, we present the nonparametric MFA model utilizing a Dirichlet process (DP) prior on the component parameters (that is, the factor loading matrix and the mean vector of each component) and describe an MCMC scheme for inference. The clustering property of the DP provides automatic selection of the number of mixture components. The latent dimensionality of each component is inferred by automatic relevance determination (ARD). Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging clustering problem. We apply our model for clustering the waveforms recorded from the cortex of a macaque monkey.

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Görür, D., Rasmussen, C. Dirichlet Process Mixtures of Factor Analysers Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

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New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams

Hill, N., Raths, C.

Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

Abstract

When considering Brain-Computer Interface (BCI) development for patients in the most severely paralysed states, there is considerable motivation to move away from BCI systems based on either motor cortex activity, or on visual stimuli. Together these account for most of current BCI research. I present the results of our recent exploration of new auditory- and tactile-stimulus-driven BCIs. The talk includes a tutorial on the construction and interpretation of classifiers which extract spatio-temporal features from event-related potential data. The effects and implications of whitening are discussed, and preliminary results on the effectiveness of a low-rank constraint (Tomioka and Aihara 2007) are shown.

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Hill, N., Raths, C. New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

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Towards Motor Skill Learning in Robotics

Peters, J.

Interactive Robot Learning - RSS workshop, June 2007 (talk)

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Peters, J. Towards Motor Skill Learning in Robotics Interactive Robot Learning - RSS workshop, June 2007 (talk)

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Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., Scheffer, T.

International Conference on Machine Learning (ICML), June 2007 (talk)

Abstract

We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

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Zien, A., Brefeld, U., Scheffer, T. Transductive Support Vector Machines for Structured Variables International Conference on Machine Learning (ICML), June 2007 (talk)

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Impact of target-to-target interval on classification performance in the P300 speller

Martens, S., Hill, J., Farquhar, J., Schölkopf, B.

Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

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Martens, S., Hill, J., Farquhar, J., Schölkopf, B. Impact of target-to-target interval on classification performance in the P300 speller Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

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Benchmarking of Policy Gradient Methods

Peters, J.

ADPRL Workshop, April 2007 (talk)

[BibTex]

Peters, J. Benchmarking of Policy Gradient Methods ADPRL Workshop, April 2007 (talk)

[BibTex]

Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning

Görür, D.

Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, April 2007, published online (phdthesis)

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Görür, D. Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning Biologische Kybernetik, Technische Universität Berlin, Berlin, Germany, April 2007, published online (phdthesis)

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Better Codes for the P300 Visual Speller

Biessmann, F., Hill, N., Farquhar, J., Schölkopf, B.

G{\"o}ttingen Meeting of the German Neuroscience Society, 7, pages: 123, March 2007 (poster)

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Biessmann, F., Hill, N., Farquhar, J., Schölkopf, B. Better Codes for the P300 Visual Speller G{\"o}ttingen Meeting of the German Neuroscience Society, 7, pages: 123, March 2007 (poster)

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Do We Know What the Early Visual System Computes?

Bethge, M., Kayser, C.

31st G{\"o}ttingen Neurobiology Conference, 31, pages: 352, March 2007 (poster)

Abstract

Decades of research provided much data and insights into the mechanisms of the early visual system. Currently, however, there is great controversy on whether these findings can provide us with a thorough functional understanding of what the early visual system does, or formulated differently, of what it computes. At the Society for Neuroscience meeting 2005 in Washington, a symposium was held on the question "Do we know that the early visual system does", which was accompanied by a widely regarded publication in the Journal of Neuroscience. Yet, that discussion was rather specialized as it predominantly addressed the question of how well neural responses in retina, LGN, and cortex can be predicted from noise stimuli, but did not emphasize the question of whether we understand what the function of these early visual areas is. Here we will concentrate on this neuro-computational aspect of vision. Experts from neurobiology, psychophysics and computational neuroscience will present studies which approach this question from different viewpoints and promote a critical discussion of whether we actually understand what early areas contribute to the processing and perception of visual information.

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Bethge, M., Kayser, C. Do We Know What the Early Visual System Computes? 31st G{\"o}ttingen Neurobiology Conference, 31, pages: 352, March 2007 (poster)

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