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Found 5 papers, 1 papers with code
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior
We show that for neural networks (NN) with normalisation layers, i. e. batch norm, layer norm, or group norm, the Laplace model evidence does not approximate the volume of a posterior mode and is thus unsuitable for model selection.
A Probabilistic Deep Image Prior over Image Space
The deep image prior regularises under-specified image reconstruction problems by reparametrising the target image as the output of a CNN.
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference
In particular, we develop a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork.
Disentangling and Learning Robust Representations with Natural Clustering
Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models.
