Publications (30)
Bayesian Learning of Sum-Product Networks
Martin Trapp, Robert Peharz, Hong Ge +2
Sum-product networks (SPNs) are flexible density estimators and have received significant attention due to their attractive inference properties. While parameter learning in SPNs i…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Robert Peharz, Steven Lang, Antonio Vergari +6
Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit a wide range of exact and efficient inference routines. Recent ``deep-learning-style'…
Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression
Zhongjie Yu, Mingye Zhu, Martin Trapp +2
Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale multi-output regression usin…
Streamlining Prediction in Bayesian Deep Learning
Rui Li, Marcus Klasson, Arno Solin +1
The rising interest in Bayesian deep learning (BDL) has led to a plethora of methods for estimating the posterior distribution. However, efficient computation of inferences, such a…
Subtractive Mixture Models via Squaring: Representation and Learning
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel +4
Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically re…