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- Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
- 2009 IEEE Conference on Computer Vision andâ€¦
- 2009

We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied inâ€¦ (More)

- Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
- IEEE Transactions on Pattern Analysis and Machineâ€¦
- 2014

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied inâ€¦ (More)

- Carl E. Rasmussen, Hannes Nickisch
- Journal of Machine Learning Research
- 2010

The GPML toolbox provides a wide range of functionality for G aussian process (GP) inference and prediction. GPs are specified by mean and covariance func tions; we offer a library of simple mean andâ€¦ (More)

- Hannes Nickisch
- 2013

We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between severalâ€¦ (More)

- Andrew Gordon Wilson, Hannes Nickisch
- ICML
- 2015

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernelâ€¦ (More)

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of theâ€¦ (More)

- Matthias W. Seeger, Hannes Nickisch
- SIAM J. Imaging Sciences
- 2011

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maximizing the posteriorâ€¦ (More)

- Matthias W. Seeger, Hannes Nickisch
- ICML
- 2008

We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative studyâ€¦ (More)

- Matthias Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard SchÃ¶lkopf
- Magnetic resonance in medicine
- 2010

The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standardâ€¦ (More)

- Matthias W. Seeger, Hannes Nickisch
- AISTATS
- 2011

We propose a novel algorithm to solve the expectation propagation relaxation of Bayesian inference for continuous-variable graphical models. In contrast to most previous algorithms, our method isâ€¦ (More)