Gaussian Processes for Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over...
Publications
View AllLearning with Kernels
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistic...
Image Style Transfer Using Convolutional Neural Networks
Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack ...
Comparing support vector machines with Gaussian kernels to radial basis function classifiers
The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial bas...
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
Assessing Approximations for Gaussian Process Classification
Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace&a...
Affiliated Researchers
Institution Info
- Type
- facility
- Country
- DE
- Publications
- 6
- Citations
- 27,921
External Links
Identifiers
- ROR
- https://ror.org/026nmvv73