PhD Machine Learning
2018-02-01
2025-12-17
Eindhoven University of Technology (TU/e)
MSc Embedded Systems
2011-09-01
2013-08-01
Eindhoven University of Technology (TU/e)
BSc Electronic Engineering
2005-08-01
2010-07-01
Universidad Nacional de Colombia (UNAL)
Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact …
Bayesian inference is a probabilistic approach to the problem of drawing conclusions from observed data. Its main challenge is computational, which the Bayesian community tends to …
Probabilistic inference is a central task in intelligent systems, enabling reasoning under uncertainty across domains such as artificial intelligence, medical diagnosis, and …
Within the realm of probabilistic graphical models, message-passing algorithms offer a powerful framework for efficient inference. When dealing with discrete variables, these …
TensorInference, a package for exact probabilistic inference in discrete graphical models, capitalizes on recent tensor network advancements. Its tensor-based engine features …
JunctionTrees.jl implements the junction tree algorithm, an efficient method to perform Bayesian inference in discrete probabilistic graphical models. It exploits Julia's …
