Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Vita Batishcheva"'
Autor:
Vita Batishcheva, Alexey Potapov
Publikováno v:
BICA
The problem of representing and learning complex visual stimuli in the context of modeling the process of conditional reflex formation is considered. The generative probabilistic framework is chosen which has been recently successfully applied to cog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bc3d6034a2ce43b12eb6c33905fac3d
https://doi.org/10.1016/j.procs.2016.07.407
https://doi.org/10.1016/j.procs.2016.07.407
Publikováno v:
Artificial General Intelligence ISBN: 9783319213644
AGI
AGI
Application of the Minimum Description Length principle to optimization queries in probabilistic programming was investigated on the example of the C++ probabilistic programming library under development. It was shown that incorporation of this crite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c077f5b9f86a804a28318f5556eea04
https://doi.org/10.1007/978-3-319-21365-1_34
https://doi.org/10.1007/978-3-319-21365-1_34
Autor:
Alexey Potapov, Vita Batishcheva
Publikováno v:
Artificial General Intelligence ISBN: 9783319213644
AGI
AGI
Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08e9eebc5f134461513f49affb801ed7
https://doi.org/10.1007/978-3-319-21365-1_2
https://doi.org/10.1007/978-3-319-21365-1_2
Publikováno v:
CCIS
The problem of bridging the gap between efficient but narrow methods of machine learning, and universal but inefficient methods was considered. Our main claim, which is methodologically important to the field of Artificial General Intelligence (AGI),
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::664074b0f9f5584c0733b9e7cfdfc4d1
https://doi.org/10.1109/ccis.2014.7175747
https://doi.org/10.1109/ccis.2014.7175747
Publikováno v:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783319125671
AIAI
AIAI
Deep learning is promising approach to extract useful nonlinear representations of data. However, it is usually applied with large training sets, which are not always available in practical tasks. In this paper, we consider stacked autoencoders with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44cf1b16a1c089bfd60d63c5fe3357a0
https://doi.org/10.1007/978-3-662-44654-6_25
https://doi.org/10.1007/978-3-662-44654-6_25