Exact Inference Optimization in Discrete Graphical Models
AI technologies are rapidly being adopted across sectors such as healthcare, finance, and manufacturing, delivering economic value through improved efficiency and automation. Much …
Probabilistic inference in the era of tensor networks and differential programming
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 …
Pushing the Boundaries of Probabilistic Inference through Message Contraction Optimization
A key aspect of intelligent systems is their capacity to reason under uncertainty. This task involves calculating probabilities of relevant variables while considering any …
Probabilistic inference using contraction of tensor networks
TensorInference, a package for exact probabilistic inference in discrete graphical models, capitalizes on recent tensor network advancements. Its tensor-based engine features …
TensorInference: A Julia package for tensor-based probabilistic inference
Probabilistic inference is a central task in intelligent systems, enabling reasoning under uncertainty across domains such as artificial intelligence, medical diagnosis, and …
Scaling Probabilistic Inference through Message Contraction Optimization
Within the realm of probabilistic graphical models, message-passing algorithms offer a powerful framework for efficient inference. When dealing with discrete variables, these …
JunctionTrees.jl - Efficient Bayesian Inference in Discrete Graphical Models
JunctionTrees.jl implements the junction tree algorithm, an efficient method to perform Bayesian inference in discrete probabilistic graphical models. It exploits Julia's …