Bayesian Inference for Spiking Neuron Models with a Sparsity Prior (2008)
Gerwinn, S., Macke, J., Seeger, M., Bethge, M., Platt, J. C., Koller, D., ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation...
Unsupervised learning of a steerable basis for invariant image representations (2007)
Bethge, M., Gerwinn, S., Macke, J.H.
There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal...
Bayesian Inference for Sparse Generalized Linear Models (2007)
Seeger, M., Gerwinn, S., Bethge, M.
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The parameters can be endowed...
Unsupervised learning of a steerable basis for invariant image representations (2007)
Bethge, M., Gerwinn, S., Macke, J.H.
There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal...