Search Results for author:
Found 9 papers, 2 papers with code
Bayesian Markov Blanket Estimation
This paper considers a Bayesian view for estimating a sub-network in a Markov random field.
Causal Compression
We propose a new method of discovering causal relationships in temporal data based on the notion of causal compression.
Greedy Structure Learning of Hierarchical Compositional Models
In this work, we consider the problem of learning a hierarchical generative model of an object from a set of images which show examples of the object in the presence of variable background clutter.
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Building on that, we show how this transformation translates to sparsity of the latent space in the new model.
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates
Estimating the causal effects of an intervention from high-dimensional observational data is difficult due to the presence of confounding.
Informed MCMC with Bayesian Neural Networks for Facial Image Analysis
Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects.
On the Difference Between the Information Bottleneck and the Deep Information Bottleneck
The actual mutual information consists of the lower bound which is optimised in DVIB and cognate models in practice and of two terms measuring how much the former requirement $T-X-Y$ is violated.
Inverse Learning of Symmetries
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Learning Conditional Invariance through Cycle Consistency
We address these shortcomings with a novel approach to cycle consistency.
