Martin Trapp

verfied profile

∙ 0 followers
TU Graz

Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations

In this work, we propose Sum-Product-Transform Networks (SPTN), an exten...
05/04/2020 ∙ by Tomáš Pevný, et al. ∙ 3 ∙ share

read it
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
04/13/2020 ∙ by Robert Peharz, et al. ∙ 10 ∙ share

read it
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

We present the preliminary high-level design and features of DynamicPPL....
02/07/2020 ∙ by Mohamed Tarek, et al. ∙ 9 ∙ share

read it
Deep Structured Mixtures of Gaussian Processes

Gaussian Processes (GPs) are powerful non-parametric Bayesian regression...
10/10/2019 ∙ by Martin Trapp, et al. ∙ 0 ∙ share

read it
Bayesian Learning of Sum-Product Networks

Sum-product networks (SPNs) are flexible density estimators and have rec...
05/26/2019 ∙ by Martin Trapp, et al. ∙ 0 ∙ share

read it
Optimisation of Overparametrized Sum-Product Networks

It seems to be a pearl of conventional wisdom that parameter learning in...
05/20/2019 ∙ by Martin Trapp, et al. ∙ 0 ∙ share

read it
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks

While Gaussian processes (GPs) are the method of choice for regression t...
09/12/2018 ∙ by Martin Trapp, et al. ∙ 0 ∙ share

read it
Probabilistic Deep Learning using Random Sum-Product Networks

Probabilistic deep learning currently receives an increased interest, as...
06/05/2018 ∙ by Robert Peharz, et al. ∙ 0 ∙ share

read it
Safe Semi-Supervised Learning of Sum-Product Networks

In several domains obtaining class annotations is expensive while at the...
10/10/2017 ∙ by Martin Trapp, et al. ∙ 0 ∙ share

read it

Martin Trapp

Featured Co-authors

Zoubin Ghahramani

Kai Xu

Guy Van den Broeck

Kristian Kersting

Franz Pernkopf

Robert Peharz

Antonio Vergari

Tomáš Pevný

Alejandro Molina

Carl E. Rasmussen

Hong Ge

Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

Deep Structured Mixtures of Gaussian Processes

Bayesian Learning of Sum-Product Networks

Optimisation of Overparametrized Sum-Product Networks

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks

Probabilistic Deep Learning using Random Sum-Product Networks

Safe Semi-Supervised Learning of Sum-Product Networks