We present a novel probabilistic multiple cause model for binary observations. In contrast to other approaches, the model is linear and it infers reasons behind both observed and ...
We consider a hierarchical two-layer model of natural signals in which both layers are learned from the data. Estimation is accomplished by Score Matching, a recently proposed est...
We present a framework for efficient, accurate approximate Bayesian inference in generalized linear models (GLMs), based on the expectation propagation (EP) technique. The paramete...
Matthias Seeger, Sebastian Gerwinn, Matthias Bethg...
As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis o...
We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...