Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
Where the Object-Oriented paradigm set about abstracting objects, iented (AO) theory draws on Psychology to abstract mentalist notions like: beliefs, perceptions, goals, and intent...
We describe a generative model for graph edges under specific degree distributions which admits an exact and efficient inference method for recovering the most likely structure. T...
—In this paper we consider an interacting two-agent sequential decision-making problem consisting of a Markov source process, a causal encoder with feedback, and a causal decoder...
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...