Zobrazeno 1 - 10
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pro vyhledávání: '"Papez, Milan"'
Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer even the most basic probabilistic inference queries without resorting
Externí odkaz:
http://arxiv.org/abs/2408.09451
Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models utilize neural networks to learn a probability distribution over undi
Externí odkaz:
http://arxiv.org/abs/2408.07394
Traditional methods for unsupervised learning of finite mixture models require to evaluate the likelihood of all components of the mixture. This becomes computationally prohibitive when the number of components is large, as it is, for example, in the
Externí odkaz:
http://arxiv.org/abs/2110.04776
Autor:
Papež, Milan, Quinn, Anthony
Bayesian transfer learning (BTL) is defined in this paper as the task of conditioning a target probability distribution on a transferred source distribution. The target globally models the interaction between the source and target, and conditions on
Externí odkaz:
http://arxiv.org/abs/2101.06884
Autor:
Papez, Milan
Publikováno v:
2016 19th International Conference on Information Fusion (FUSION); 2016, p1063-1070, 8p
Autor:
Papez, Milan
Publikováno v:
2016 17th International Carpathian Control Conference (ICCC); 2016, p545-551, 7p