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Found 7 papers, 2 papers with code
A Unified View of Algorithms for Path Planning Using Probabilistic Inference on Factor Graphs
Even if path planning can be solved using standard techniques from dynamic programming and control, the problem can also be approached using probabilistic inference.
Path Planning Using Probability Tensor Flows
In this paper, probability propagation is applied to model agent's motion in potentially complex scenarios that include goals and obstacles.
Intent Classification in Question-Answering Using LSTM Architectures
Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI).
An Analysis of Word2Vec for the Italian Language
Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text.
Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model
Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs.
Discrete Independent Component Analysis (DICA) with Belief Propagation
We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables.
Towards Building Deep Networks with Bayesian Factor Graphs
We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice.
