Header logo is


2012


no image
Accelerating Nearest Neighbor Search on Manycore Systems

Cayton, L.

In Parallel Distributed Processing Symposium (IPDPS), 2012 IEEE 26th International, pages: 402-413, IPDPS, May 2012 (inproceedings)

Abstract
We develop methods for accelerating metric similarity search that are effective on modern hardware. Our algorithms factor into easily parallelizable components, making them simple to deploy and efficient on multicore CPUs and GPUs. Despite the simple structure of our algorithms, their search performance is provably sublinear in the size of the database, with a factor dependent only on its intrinsic dimensionality. We demonstrate that our methods provide substantial speedups on a range of datasets and hardware platforms. In particular, we present results on a 48-core server machine, on graphics hardware, and on a multicore desktop.

ei

Web DOI [BibTex]

2012


Web DOI [BibTex]


no image
PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits

Seldin, Y., Cesa-Bianchi, N., Auer, P., Laviolette, F., Shawe-Taylor, J.

In JMLR Workshop and Conference Proceedings 26, pages: 98-111, JMLR, Cambridge, MA, USA, On-line Trading of Exploration and Exploitation 2, April 2012 (inproceedings)

Abstract
We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The first ingredient is a new concentration inequality that makes it possible to control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. The second ingredient is an application of this inequality to the exploration-exploitation trade-off via importance weighted sampling. We apply the new tool to the stochastic multiarmed bandit problem, however, the main importance of this paper is the development and understanding of the new tool rather than improvement of existing algorithms for stochastic multiarmed bandits. In the follow-up work we demonstrate that the new tool can improve over state-of-the-art in structurally richer problems, such as stochastic multiarmed bandits with side information (Seldin et al., 2011a).

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Hierarchical Relative Entropy Policy Search

Daniel, C., Neumann, G., Peters, J.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 273-281, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS, April 2012 (inproceedings)

Abstract
Many real-world problems are inherently hierarchically structured. The use of this structure in an agent's policy may well be the key to improved scalability and higher performance. However, such hierarchical structures cannot be exploited by current policy search algorithms. We will concentrate on a basic, but highly relevant hierarchy - the `mixed option' policy. Here, a gating network fi rst decides which of the options to execute and, subsequently, the option-policy determines the action. In this paper, we reformulate learning a hierarchical policy as a latent variable estimation problem and subsequently extend the Relative Entropy Policy Search (REPS) to the latent variable case. We show that our Hierarchical REPS can learn versatile solutions while also showing an increased performance in terms of learning speed and quality of the found policy in comparison to the nonhierarchical approach.

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Movement Segmentation and Recognition for Imitation Learning

Meier, F., Theodorou, E., Schaal, S.

In Seventeenth International Conference on Artificial Intelligence and Statistics, La Palma, Canary Islands, Fifteenth International Conference on Artificial Intelligence and Statistics , April 2012 (inproceedings)

am

link (url) [BibTex]

link (url) [BibTex]


no image
Personalized medicine: from genotypes and molecular phenotypes towards computed therapy

Stegle, O., Roth, FP., Morris, Q., Listgarten, J.

In pages: 323-326, (Editors: Altman, R.B. , A.K. Dunker, L. Hunter, T. Murray, T.E. Klein), World Scientific Publishing, Singapore, Pacific Symposium on Biocomputing (PSB), January 2012 (inproceedings)

Abstract
Joint genotyping and large-scale phenotyping of molecular traits are currently available for a number of important patient study cohorts and will soon become feasible in routine medical practice. These data are one component of several that are setting the stage for the development of personalized medicine, promising to yield better disease classification, enabling more specific treatment, and also allowing for improved preventive medical screening. This conference session explores statistical challenges and new opportunities that arise from application of genome-scale experimentation for personalized genomics and medicine.

ei

Web DOI [BibTex]

Web DOI [BibTex]


no image
Approximate Gaussian Integration using Expectation Propagation

Cunningham, J., Hennig, P., Lacoste-Julien, S.

In pages: 1-11, -, January 2012 (inproceedings) Submitted

Abstract
While Gaussian probability densities are omnipresent in applied mathematics, Gaussian cumulative probabilities are hard to calculate in any but the univariate case. We offer here an empirical study of the utility of Expectation Propagation (EP) as an approximate integration method for this problem. For rectangular integration regions, the approximation is highly accurate. We also extend the derivations to the more general case of polyhedral integration regions. However, we find that in this polyhedral case, EP's answer, though often accurate, can be almost arbitrarily wrong. These unexpected results elucidate an interesting and non-obvious feature of EP not yet studied in detail, both for the problem of Gaussian probabilities and for EP more generally.

ei pn

Web [BibTex]

Web [BibTex]


no image
Kernel Topic Models

Hennig, P., Stern, D., Herbrich, R., Graepel, T.

In Fifteenth International Conference on Artificial Intelligence and Statistics, 22, pages: 511-519, JMLR Proceedings, (Editors: Lawrence, N. D. and Girolami, M.), JMLR.org, AISTATS , 2012 (inproceedings)

Abstract
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions. This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. The main challenge is efficient approximate inference on the latent Gaussian. We present an approximate algorithm cast around a Laplace approximation in a transformed basis. The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas.

ei pn

PDF Web [BibTex]

PDF Web [BibTex]


no image
Structured Apprenticeship Learning

Boularias, A., Kroemer, O., Peters, J.

In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2012 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Blind Correction of Optical Aberrations

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

In Computer Vision - ECCV 2012, LNCS Vol. 7574, pages: 187-200, (Editors: A Fitzgibbon, S Lazebnik, P Perona, Y Sato, and C Schmid), Springer, Berlin, Germany, 12th IEEE European Conference on Computer Vision, ECCV, 2012 (inproceedings)

Abstract
Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While traditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of optical flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution. In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blindly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the-art non-blind approaches.

ei

PDF Web DOI [BibTex]

PDF Web DOI [BibTex]


no image
Interactive Domain Adaptation Technique for the Classification of Remote Sensing Images

Persello, C., Dinuzzo, F.

In IEEE International Geoscience and Remote Sensing Symposium , pages: 6872-6875, IEEE, IGARSS, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Point Cloud Completion Using Symmetries and Extrusions

Kroemer, O., Ben Amor, H., Ewerton, M., Peters, J.

In IEEE-RAS International Conference on Humanoid Robots , pages: 680-685, IEEE, HUMANOIDS, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
The representer theorem for Hilbert spaces: a necessary and sufficient condition

Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 189-196, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Same, same, but different: EEG correlates of n-back and span working memory tasks

Scharinger, C., Cienak, G., Walter, C., Zander, TO., Gerjets, P.

In Proceedings of the 48th Congress of the German Society for Psychology, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Probabilistic Modeling of Human Movements for Intention Inference

Wang, Z., Deisenroth, M., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J.

In Proceedings of Robotics: Science and Systems VIII, pages: 8, R:SS, 2012 (inproceedings)

Abstract
Inference of human intention may be an essential step towards understanding human actions [21] and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human behaviors/actions and we introduce an approximate inference algorithm to efficiently infer the human’s intention from an ongoing action. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-the-art regression and classification.

ei

PDF link (url) [BibTex]

PDF link (url) [BibTex]


no image
Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise

Deisenroth, M., Peters, J.

In The 10th European Workshop on Reinforcement Learning (EWRL), 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
On Causal and Anticausal Learning

Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1255-1262, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Learning from distributions via support measure machines

Muandet, K., Fukumizu, K., Dinuzzo, F., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 10-18, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Scalable nonconvex inexact proximal splitting

Sra, S.

In Advances of Neural Information Processing Systems 25, pages: 539-547, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A min-cut solution to mapping phenotypes to networks of genetic markers

Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.

In 17th Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2012 (inproceedings) Submitted

ei

[BibTex]

[BibTex]


no image
Efficiently mapping phenotypes to networks of genetic loci

Azencott, C., Grimm, D., Kawahara, Y., Borgwardt, K.

In NIPS Workshop on Machine Learning in Computational Biology (MLCB), 2012 (inproceedings) Submitted

ei

[BibTex]

[BibTex]


no image
Modelling transition dynamics in MDPs with RKHS embeddings

Grünewälder, S., Lever, G., Baldassarre, L., Pontil, M., Gretton, A.

In Proceedings of the 29th International Conference on Machine Learning, pages: 535-542, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Clustering: Science or Art?

von Luxburg, U., Williamson, R., Guyon, I.

In JMLR Workshop and Conference Proceedings, Volume 27, pages: 65-79, Workshop on Unsupervised Learning and Transfer Learning, 2012 (inproceedings)

Abstract
We examine whether the quality of di erent clustering algorithms can be compared by a general, scienti cally sound procedure which is independent of particular clustering algorithms. We argue that the major obstacle is the diculty in evaluating a clustering algorithm without taking into account the context: why does the user cluster his data in the rst place, and what does he want to do with the clustering afterwards? We argue that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Di erent techniques to evaluate clustering algorithms have to be developed for di erent uses of clustering. To simplify this procedure we argue that it will be useful to build a \taxonomy of clustering problems" to identify clustering applications which can be treated in a uni ed way and that such an e ort will be more fruitful than attempting the impossible | developing \optimal" domain-independent clustering algorithms or even classifying clustering algorithms in terms of how they work.

ei

PDF [BibTex]

PDF [BibTex]


no image
A Brain-Robot Interface for Studying Motor Learning after Stroke

Meyer, T., Peters, J., Brötz, D., Zander, T., Schölkopf, B., Soekadar, S., Grosse-Wentrup, M.

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 4078 - 4083 , IEEE, Piscataway, NJ, USA, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Generalization of Human Grasping for Multi-Fingered Robot Hands

Ben Amor, H., Kroemer, O., Hillenbrand, U., Neumann, G., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 2043-2050, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Learning Concurrent Motor Skills in Versatile Solution Spaces

Daniel, C., Neumann, G., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 3591-3597, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Learning to Select and Generalize Striking Movements in Robot Table Tennis

Mülling, K., Kober, J., Kroemer, O., Peters, J.

In AAAI Fall Symposium on Robots Learning Interactively from Human Teachers, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Computational vascular morphometry for the assessment of pulmonary vascular disease based on scale-space particles

Estépar, R., Ross, J., Krissian, K., Schultz, T., Washko, G., Kindlmann, G.

In pages: 1479-1482, IEEE, 9th International Symposium on Biomedical Imaging (ISBI) , 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Hilbert space embedding for Dirichlet Process mixtures

Muandet, K.

In NIPS Workshop on confluence between kernel methods and graphical models, 2012 (inproceedings)

ei

arXiv [BibTex]

arXiv [BibTex]


no image
Causal discovery with scale-mixture model for spatiotemporal variance dependencies

Chen, Z., Zhang, K., Chan, L.

In Advances in Neural Information Processing Systems 25, pages: 1736-1744, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF PDF [BibTex]

PDF PDF [BibTex]


no image
Bridging Offline and Online Social Graph Dynamics

Gomez Rodriguez, M., Rogati, M.

In 21st ACM Conference on Information and Knowledge Management, pages: 2447-2450, (Editors: Chen, X., Lebanon, G., Wang, H. and Zaki, M. J.), ACM, CIKM, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
A new metric on the manifold of kernel matrices with application to matrix geometric means

Sra, S.

In Advances in Neural Information Processing Systems 25, pages: 144-152, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Conditional mean embeddings as regressors

Grünewälder, S., Lever, G., Baldassarre, L., Patterson, S., Gretton, A., Pontil, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1823-1830, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Shortest path distance in random k-nearest neighbor graphs

Alamgir, M., von Luxburg, U.

In Proceedings of the 29th International Conference on Machine Learning, International Machine Learning Society, International Conference on Machine Learning (ICML), 2012 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Toward Fast Policy Search for Learning Legged Locomotion

Deisenroth, M., Calandra, R., Seyfarth, A., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 1787-1792, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Robot Skill Learning

Peters, J., Kober, J., Mülling, K., Nguyen-Tuong, D., Kroemer, O.

In 20th European Conference on Artificial Intelligence , pages: 40-45, ECAI, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Towards a learning-theoretic analysis of spike-timing dependent plasticity

Balduzzi, D., Besserve, M.

In Advances in Neural Information Processing Systems 25, pages: 2465-2473, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Recording and Playback of Camera Shake: Benchmarking Blind Deconvolution with a Real-World Database

Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.

In Computer Vision - ECCV 2012, LNCS Vol. 7578, pages: 27-40, (Editors: A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid), Springer, Berlin, Germany, 12th European Conference on Computer Vision, ECCV , 2012 (inproceedings)

Abstract
Motion blur due to camera shake is one of the predominant sources of degradation in handheld photography. Single image blind deconvolution (BD) or motion deblurring aims at restoring a sharp latent image from the blurred recorded picture without knowing the camera motion that took place during the exposure. BD is a long-standing problem, but has attracted much attention recently, cumulating in several algorithms able to restore photos degraded by real camera motion in high quality. In this paper, we present a benchmark dataset for motion deblurring that allows quantitative performance evaluation and comparison of recent approaches featuring non-uniform blur models. To this end, we record and analyse real camera motion, which is played back on a robot platform such that we can record a sequence of sharp images sampling the six dimensional camera motion trajectory. The goal of deblurring is to recover one of these sharp images, and our dataset contains all information to assess how closely various algorithms approximate that goal. In a comprehensive comparison, we evaluate state-of-the-art single image BD algorithms incorporating uniform and non-uniform blur models.

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Towards identifying and validating cognitive correlates in a passive Brain-Computer Interface for detecting Loss of Control

Zander, TO., Pape, AA.

In Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, EMBC, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Neural correlates of workload and puzzlement during loss of control

Pape, AA., Gerjets, P., Zander, TO.

In Meeting of the EARLI SIG 22 Neuroscience and Education, 2012 (inproceedings)

ei

[BibTex]

[BibTex]


no image
Hypothesis testing using pairwise distances and associated kernels

Sejdinovic, D., Gretton, A., Sriperumbudur, B., Fukumizu, K.

In Proceedings of the 29th International Conference on Machine Learning, pages: 1111-1118, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Efficient Training of Graph-Regularized Multitask SVMs

Widmer, C., Kloft, M., Görnitz, N., Rätsch, G.

In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML/PKDD 2012, LNCS Vol. 7523, pages: 633-647, (Editors: PA Flach and T De Bie and N Cristianini), Springer, Berlin, Germany, ECML, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]


no image
Hilbert Space Embeddings of POMDPs

Nishiyama, Y., Boularias, A., Gretton, A., Fukumizu, K.

In Conference on Uncertainty in Artificial Intelligence (UAI), 2012 (inproceedings)

ei

PDF Web [BibTex]

PDF Web [BibTex]


no image
Learning Throwing and Catching Skills

Kober, J., Mülling, K., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages: 5167-5168, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Maximally Informative Interaction Learning for Scene Exploration

van Hoof, H., Kroemer, O., Ben Amor, H., Peters, J.

In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages: 5152-5158, IROS, 2012 (inproceedings)

ei

PDF DOI [BibTex]

PDF DOI [BibTex]


no image
Investigating the Neural Basis of Brain-Computer Interface (BCI)-based Stroke Rehabilitation

Meyer, T., Peters, J., Zander, T., Brötz, D., Soekadar, S., Schölkopf, B., Grosse-Wentrup, M.

In International Conference on NeuroRehabilitation (ICNR) , pages: 617-621, (Editors: JL Pons, D Torricelli, and M Pajaro), Springer, Berlin, Germany, ICNR, 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function

Ortega, P., Grau-Moya, J., Genewein, T., Balduzzi, D., Braun, D.

In Advances in Neural Information Processing Systems 25, pages: 3014-3022, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Algorithms for Learning Markov Field Policies

Boularias, A., Kroemer, O., Peters, J.

In Advances in Neural Information Processing Systems 25, pages: 2186-2194, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Semi-Supervised Domain Adaptation with Copulas

Lopez-Paz, D., Hernandez-Lobato, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 25, pages: 674-682, (Editors: P Bartlett, FCN Pereira, CJC. Burges, L Bottou, and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
Gradient Weights help Nonparametric Regressors

Kpotufe, S., Boularias, A.

In Advances in Neural Information Processing Systems 25, pages: 2870-2878, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

ei

PDF [BibTex]

PDF [BibTex]


no image
A Blind Deconvolution Approach for Pseudo CT Prediction from MR Image Pairs

Hirsch, M., Hofmann, M., Mantlik, F., Pichler, B., Schölkopf, B., Habeck, M.

In 19th IEEE International Conference on Image Processing (ICIP) , pages: 2953 -2956, IEEE, ICIP, 2012 (inproceedings)

ei

DOI [BibTex]

DOI [BibTex]