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stat.ML2023
A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
Tim Z. Xiao, Weiyang Liu, Robert Bamler
Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications. Since exact Ba…
cs.CV2023★ 1 cited
The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch
Tim Z. Xiao, Johannes Zenn, Robert Bamler
The Street View House Numbers (SVHN) dataset is a popular benchmark dataset in deep learning. Originally designed for digit classification tasks, the SVHN dataset has been widely u…
stat.ML2023
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?
Tim Z. Xiao, Johannes Zenn, Robert Bamler
Variational autoencoders (VAEs) are deep probabilistic models that are used in scientific applications. Many works try to mitigate this problem from the probabilistic methods persp…