Lifted probabilistic inference exploits symmetries in a probabilistic mo...
We describe a Lagrange-Newton framework for the derivation of learning r...
DNA-based nanonetworks have a wide range of promising use cases, especia...
Fully symmetric learning rules for principal component analysis can be
d...
Neural learning rules for principal component / subspace analysis (PCA /...
In coupled learning rules for PCA (principal component analysis) and SVD...
Large probabilistic models are often shaped by a pool of known individua...
Evidence often grounds temporal probabilistic relational models over tim...
The lifted dynamic junction tree algorithm (LDJT) efficiently answers
fi...
The lifted dynamic junction tree algorithm (LDJT) efficiently answers
fi...
Standard approaches for inference in probabilistic formalisms with
first...
The term "affordance" denotes the behavioral meaning of objects. We prop...
Real-time analytics that requires integration and aggregation of
heterog...