research
∙
05/25/2023
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits
Some of the most successful knowledge graph embedding (KGE) models for l...
share
research
∙
02/23/2023
Bayesian Structure Scores for Probabilistic Circuits
Probabilistic circuits (PCs) are a prominent representation of probabili...
research
∙
09/21/2022
Continuous Mixtures of Tractable Probabilistic Models
Probabilistic models based on continuous latent spaces, such as variatio...
research
∙
06/04/2022
Active Bayesian Causal Inference
Causal discovery and causal reasoning are classically treated as separat...
research
∙
07/11/2020
Towards Robust Classification with Deep Generative Forests
Decision Trees and Random Forests are among the most widely used machine...
research
∙
06/25/2020
Joints in Random Forests
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminativ...
research
∙
04/13/2020
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits
Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
research
∙
01/07/2020
Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
research
∙
12/20/2019
Sum-Product Network Decompilation
There exists a dichotomy between classical probabilistic graphical model...
research
∙
10/10/2019
Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression...
research
∙
05/26/2019
Bayesian Learning of Sum-Product Networks
Sum-product networks (SPNs) are flexible density estimators and have rec...
research
∙
05/21/2019
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
Bayesian networks are a central tool in machine learning and artificial ...
research
∙
05/20/2019
Optimisation of Overparametrized Sum-Product Networks
It seems to be a pearl of conventional wisdom that parameter learning in...
research
∙
01/11/2019
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
We introduce SPFlow, an open-source Python library providing a simple in...
research
∙
12/05/2018
Efficient and Robust Machine Learning for Real-World Systems
While machine learning is traditionally a resource intensive task, embed...
research
∙
09/30/2018
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
While deep neural networks are a highly successful model class, their la...
research
∙
09/12/2018
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
While Gaussian processes (GPs) are the method of choice for regression t...
research
∙
07/24/2018
Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a ...
research
∙
06/05/2018
Probabilistic Deep Learning using Random Sum-Product Networks
Probabilistic deep learning currently receives an increased interest, as...
research
∙
10/10/2017
Safe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the...
research
∙
01/22/2016
On the Latent Variable Interpretation in Sum-Product Networks
One of the central themes in Sum-Product networks (SPNs) is the interpre...
research
∙
06/27/2012